Department of Landscape, Spatial and Infrastructure Sciences Institute for Transport Studies Head of the institute: Gerd Sammer Advisory team: Gerd Sammer, Panagiotis Papaioannou DISCRETE CHOICE MODEL FOR A NEW ECO CITY CAR Dissertation submitted to attain the doctor’s degree at the University of Natural Resources and Life Sciences, Vienna Submitted by Sandra Wegener Vienna, March 2011 To Emma & Marlies 3 Acknowledgments I would like to express my gratitude to all those people who supported my work and played a role in finishing my dissertation: Prof. Gerd Sammer for his technical and scientific steering of the research; Prof. Panos Papaioannou for agreeing to review the thesis; my colleague Alexander Neumann for his teamwork in the CLEVER project and his motivation to carry on; my colleague Reinhard Hössinger for his statistical support; my colleagues Juliane Stark and Wiekbe Unbehaun for sharing my worries; my sister Doris Hanzl for reading and correcting this work. Ein ganz besonders Anliegen ist es mir, mich bei meinen Eltern Karl und Ingrid Hanzl für Ihre Unterstützung und für die kostbare Zeit zu bedanken, die sie mir verschafft haben, indem sie auf meine beiden Kinder, Emma und Marlies, aufgepasst haben. Ein dickes Bussi auch an Emma und Marlies, die einerseits der Grund dafür waren, dass die Fertigstellung meiner Dissertation etwas länger gedauert hat, als ursprünglich geplant, aber mir andererseits immer wieder die Kraft gegeben haben, nicht aufzugeben. Nicht zuletzt gilt mein Dank meinem Mann Stefan, für seine Liebe und Geduld und dafür, dass er mich in meinen Bestrebungen, mein Ziel zu realisieren, stets bestärkt hat. Danke! The thesis is based on the EU-project CLEVER „Compact Low Emission Vehicle for Urban Transport“, which was funded by the European Commission under the Competitive and Sustainable Growth Programme of the Fifth Framework Programme from 2002 to 2006. Many thanks to all project partners and subcontractors: Technical University of Berlin, Institut fuer Land- und Seeverkehr (TUB), Berlin (D) BMW Bayerische Motoren Werke AG (BMW), Munich (D) Cooper Avon Tyres (UK) ARC Leichtmetall Kompetenzzentrum Ranshofen GmbH (LKR), Ranshofen (A) Institut Français du Pétrole (IFP), Vernaison (F) Takata-Petri AG (TP), Berlin (D) University of Bath, Department of Mechanical Engineering (UBAH.MECH), Bath (UK) University for Bodenkultur Vienna, Institute for Transport Studies (BOKU-ITS), Vienna (A) WEH GmbH (WEH), Illertissen (D) TRIAS SA, Thessaloniki (GR) ZIS + P Verkehrsplanung, Graz (A) 4 Foreword „A lot of traditional economists would say that feelings are the tip of the iceberg, and that rational thought is what lies below, determining your choice. Neuroscience would say that the huge part of the iceberg is the feelings [D’ANTONIO, 2004].” Each day reams of decisions are made by each of us. While many of them are taken newly every time, some of them get a force of habit. Mode choice for daily trips is generally neither a spur of the moment decision nor is it made each day anew. It is usually a sequence of habits, tried and well-proven behaviour and/or affected by certain constraints. Feelings and subjective perspectives as well as the inner willingness play a crucial role in this choice process. Recent mode choice models seek to consider these factors and are improved in a way including subjetive perspectives and individual attitudes. Nevertheless, measurable attributes of alternatives (travel costs and travel time, which corresponds to generalized travel costs) are still the basis for mode choice in surveying hypothetical behaviour (stated preference). As a result it has to be noticed that the influence of travel costs is very often overestimated, while the argument of time savings predominates. Investigating the market potential of an innovative, new type of vehicle a stated preference (SP) survey is the appropriate method – provided that the quantity of (new) choices is checked critically against a realistic quality of answers and not leads to an overestimation of the market potential. That is the difficulty and challenge of designing and analysing an SP survey, to verify the compliance of hypothetical (stated) behaviour with actual future behaviour. Although several years have elapsed since the beginning and finishing of this research, and development of innovative vehicle and mobility concepts has not stopped, the problems and questions are still current and worth to be examined: “How do mobile persons act? What kind of variables, objective as well as subjective ones, have an influence on mode choice? Which kind of measures or scenarios may induce a change in mobility behaviour? Which is the best method to survey and analyse future travel behaviour? And to what extent are innovative eco city cars accpted by the motorists?” As alternative engines and technologies have already reached a high standard and make a vital contribution to an environmentally friendly transport, it is crucial that these new concepts are widely accepted. Thus it is on the automobile industry to manufacture and promote a comparable range of new eco city cars, because despite the individuality of the motorits in this regard conformity is sought after. Not least it is up to politics to support and favour new individual concepts to induce a change in travel behaviour for the benefit of urban traffic and the environment. 5 Abstract Individual mobility, more precisely motorised mobility, has gained in importance over the past years. With the constant growth of motorised city traffic, various problems arise from an increase of exhaust, noise and CO2 emissions to the cumulative consumption of urban space and energy, affecting people’s quality of life. As it seems to be really challenging to move car drivers to other eco-friendly modes, like public transport or bicycle, as various attempts have barely succeeded, one approach might be to accept their demand for individual motorised mobility but under the condition of favouring eco friendly cars. To be able to come up with this claim, there must be a satisfactory range of outstanding, innovative eco cars on the one hand, and on the other hand the political frame for the use of these types of cars has to be set up forcefully. Within the scope of the EU-project CLEVER “Compact low emission vehicle for urban transport” a three-wheeled eco city car was conjointly developed by partners of the automotive industry and of three European universities. The outcome of this co-operation was a small, trendy city car (as a prototype) with an exceptional design and various technical features (tilting mechanism, removable gas cylinder etc.), powered by a CNG (compressed natural gas) engine. The consequential matter of interest of the author was to what extent this new car appeals to customers; and what are the benefits for the environment resulting from its use. To pursue these questions a two-stage mobility survey in the Austrian case study city Graz was conducted. In a revealed preference (RP) survey the actual daily mobility pattern of the respondents were looked at, followed by a tailored stated preference (SP) survey to explore the hypothetical use of the new car under scenario conditions. The outcome was first analysed in a descriptive analysis, demonstrating the potential mode shift towards the new car, which was ranged between 1,2% and 1,4%. The impacts on mode choice, in fact the influencing attributes, were analysed by means of a discrete choice model. In a stepwise procedure various types of Logit models were estimated, including attributes of alternatives (travel costs and travel time), socio-demographic characteristics (gender, age, and income), attitudes and subjective motives. It was evident that the attribute travel cost had no influence on mode choice at all, while for travel time a significant influence was verified. The unobserved attractiveness of the alternative car, covered in the alternative specific constant (ASC), was noticeable as well, exceeding the explanatory power of all the other variables. As the problem of hypothetical surveys is generally, how realistic the answers of the interviewees are, the hypothetical mode choice resulting from the SP survey was checked according to its consistency; and as a consequence the data were corrected to an assumed more realistic choice, resulting in improved models, which justified the step of correction. After verifying the potential of the new eco car, the benefits for the environment and for urban traffic resulting from its use were of special interest and were estimated by means of a costbenefit analysis. Effects on CO2 emissions, air pollution, fuel consumption, noise, road accidents, travel time and infrastructure were analysed. It became obvious that an improvement of single issues (e.g. 3% reduction of CO2) could only be achieved by shifting the mode choice from motorised modes (car drivers and motorcyclists) to the new eco car, as in all the other cases (car passengers, public transport and bicycle) the positive effect of the eco car turned to a negative one. 6 Concluding, it can be said that the ambitious efforts of the automotive manufacturers in developing eco city cars are a step in the right direction to assume responsibility for people and the environment; therefore, it remains to the politics to support these kinds of cars and finally to the users to accept the offer. Key words: Eco city car, CNG, Stated preference survey, Mode choice model, Logit model, Cost-benefit analysis 7 Kurzfassung Individuelle Mobilität, genauergesagt motorisierte individuelle Mobilität, hat über die Jahre hinweg stetig an Bedeutung gewonnen. Allerdings gehen mit der zunehmenden Motorisierung in den Städten eine Reihe von Problemen einher, angefangen von einem Anstieg der Schadstoff- und CO2 Emissionen, über zunehmenden Lärm bis hin zum Verbrauch von Energie und Flächen, die die Lebensqualität der Stadtbewohner erheblich beeinträchtigen. Da es scheinbar eine schwierige und kaum zu lösende Herausforderung ist, Autofahrer für die Nutzung umweltfreundlicher Verkehrsmittel zu begeistern, müssen andere Lösungen zur Bewältigung der genannten Probleme gefunden werden. Statt Autofahrer mit Restriktionen und finanziellen Sanktionen zu belegen, ist ein Ansatz, durch Förderung und Bewerbung umweltfreundlicher Autos, den Wunsch nach induvidueller Mobilität zu akzeptieren und ihm gerecht zu werden. Dies setzt allerdings voraus, dass es ein zufriedenstellendes Angebot an innovativen Automodellen auf dem Markt gibt, und die politischen Rahmenbedingungen für die Nutzung dieser Stadtautos vorhanden sind. Im Rahmen des EU-Projektes CLEVER „Compact low emission vehicle for urban transport“ kooperierten Partner der Autoindustrie und drei europäische Universitäten, mit dem Ziel, ein umweltfreundliches Stadtauto zu entwickeln. Das Produkt dieser Zusammenarbeit war ein Prototyp eines kleinen, trendigen, dreirädrigen Stadtautos mit Erdgasantrieb (CNGcommpressed natrural gas), mit außergewöhnlichem Design und zahlreichen technischen Besonderheiten (Neigungsmechanismus bei Kurvenfahrten, herausnehmbare Druckgasbehälter etc.). Im Zentrum des Forschungsinteresses der Autorin stand die Frage nach dem potentiellen Ausmaß der Nutzung durch die Kunden und dem daraus folgenden Nutzen für die Umwelt. Dazu wurde in der österreichischen Modellstadt Graz eine zweistufige Mobilitätsbefragung, bestehend aus einer revealed preference (RP) und einer stated preference (SP) Befragung, durchgeführt. In einer deskriptiven Analyse wurde der potentielle Umstieg auf das neue Stadtauto im Zuge der hypothetischen, szennarienbedingten Verkehrsmittelverlagerung analysiert und mit 1,2% bis 1,4% berechnet. Mittels Wahlverhaltensmodell wurden die Einflussfaktoren auf die Verkehrsmittelwahl (Stadtauto oder konventioneller Pkw) analysiert. In Abhängigkeit von den unterschiedlichen Merkmalen (Attribute der Alternativen, soziodemographische Charakteristika, Einstellungen) wurden in einem schrittweisen Prozess mehrere Logit Modelle berechnet. Hauptaugenmerk lag dabei auf den Einfluss der Attribute der Alternativen, Reisezeit und Reisekosten. Ausschlaggebend für die Wahl des Stadtautos war in erster Linie die Reisezeit, während für die Reisekosten kein signifikanter Einfluss nachgewiesen werden konnte. Da das generelle Problem von hypothetischen Befragungen die Frage nach der Glaubwürdigkeit und Wirklichkeitsnähe der Ergebnisse ist, wurde die Plausibilität der Antworten genau überprüft und als Konsequenz korrigiert. Daraus entstand ein korrigiertes Sample, das ebenfalls modelliert wurde und verbesserte Modellergebnisse hervorbrachte. Nach Verifizierung des Marktpotentials des neuen Stadtautos wurde dessen Nutzen für die Umwelt und für den Stadtverkehr mittels Kosten-Nutzen Analyse abgeschätzt. Die untersuchten Auswirkungen umfassten CO2 Emissionen und Emissionen von Luftschadstoffen, Energieverbrauch, Lärm, Verkehrsunfälle, Reisezeit und Infrastrukturmaßnahmen. Es zeigte sich, dass eine Verbesserung der einzelnen Bereiche (z.B. 3% weniger CO2 und Luftschadstoffe) nur dann erwartet werden kann, wenn die Verkehrsmittelverlagerung auf das neue Stadtauto ausschließlich vom motorisierten Individualverkehr (Pkw- oder Motorradfahrer) erfolgte. In allen anderen Fällen (Pkw- 8 Mitfahrer, Radfahrer, Benutzer von öffentlichen Verkehrsmitteln) würde sich der positive Effekt, durch die Zunahme des motorisierten Individualverkehrs, umkehren. Abschließend kann festgehalten werden, dass die ambitionierten Bestrebungen der Automobilindustrie, umweltfreundliche Fahrzeuge zu entwickeln, ein Schritt in die richtige Richtung sind, zusammen mit der Politik, Verantwortung für die Gesellschaft und für die Umwelt zu übernehmen – bleiben nur noch die Nutzer, an denen es letztendlich liegt, das Angebot anzunehmen und ihren Teil zu einem stadtverträglicheren Verkehr beizutragen. Schlüsselwörter: Umweltfreundliches Stadtauto, Erdgas, Stated preference Befragung, Verkehrsmittelwahlverhalten, Logit Model, Kosten-Nutzen Analyse 9 Table of contents 1 2 Introduction ___________________________________________________ 14 1.1 The Problem _____________________________________________________ 14 1.2 Objectives and aims________________________________________________ 14 1.3 Setting of the research ______________________________________________ 15 1.4 Structure of the report ______________________________________________ 16 Literature review on the use of small eco city cars ___________________ 18 2.1 Classification of vehicles ____________________________________________ 18 2.2 Reducing CO2 emissions ____________________________________________ 21 2.2.1 European Transport Policy _________________________________________ 21 2.2.1.1 Green Paper by the European Commission ________________________ 21 2.2.1.2 CO2 legislation_______________________________________________ 21 2.2.2 Position of the automobile industry __________________________________ 22 2.2.3 Alternative fuels and their role in the growing vehicle fleet ________________ 23 2.3 Demands on small eco city cars ______________________________________ 26 2.3.1 Manufacturers’ approach __________________________________________ 26 2.3.2 Users‘ requirements ______________________________________________ 27 2.3.3 Policy actions ___________________________________________________ 29 2.4 Vehicle concepts and concept cars ____________________________________ 31 2.4.1 Mazda Kiyora ___________________________________________________ 31 2.4.2 Renault Twizy Z.E. _______________________________________________ 32 2.4.3 GM EN-V Concept _______________________________________________ 33 2.4.4 MIT CityCar ____________________________________________________ 35 3 4 CLEVER – Compact Low Emission Vehicle for Urban Transport ________ 37 3.1 CLEVER Project __________________________________________________ 37 3.2 CLEVER Vehicle concept ___________________________________________ 37 Methodology __________________________________________________ 41 4.1 Research Flow ____________________________________________________ 41 4.2 Selection of the Case Study City ______________________________________ 42 4.3 Definition of Scenarios ______________________________________________ 42 4.4 Survey Method ____________________________________________________ 42 4.4.1 Revealed Preference Survey _______________________________________ 43 4.4.2 Stated Preference Survey _________________________________________ 46 4.5 Data Checks and Quality Control ______________________________________ 47 4.6 Data Analysis _____________________________________________________ 48 10 4.6.1 Descriptive and analytic statistics ____________________________________ 48 4.6.2 Discrete Choice Analysis __________________________________________ 49 4.6.3 Evaluation of Effects and Impacts ___________________________________ 50 5 Sampling _____________________________________________________ 51 5.1 Gross and Net Sample ______________________________________________ 51 5.1.1 Revealed Preference Survey _______________________________________ 51 5.1.2 Stated Preference Survey _________________________________________ 51 6 7 5.2 Weighting and Grossing Up __________________________________________ 52 5.3 Random Error of the sample _________________________________________ 53 Case study city Graz ____________________________________________ 56 6.1 General Characteristics _____________________________________________ 56 6.2 Transport Policy, Infrastructure and Organisation _________________________ 56 SP Survey _____________________________________________________ 58 7.1 The in-depth interview ______________________________________________ 58 7.1.1 Interview process ________________________________________________ 58 7.1.2 Questionnaires and survey materials _________________________________ 59 7.1.3 Role of the interviewer ____________________________________________ 60 7.2 Design of the Stated Choice Experiment ________________________________ 61 7.2.1 Tailoring Stated Choice ___________________________________________ 61 7.2.2 Scenarios of the SP Survey ________________________________________ 63 7.2.2.1 Scenario A__________________________________________________ 64 7.2.2.2 Scenario B__________________________________________________ 65 7.2.2.3 Scenario C _________________________________________________ 66 7.2.3 Alternative modes in the scenarios __________________________________ 66 7.2.4 Assumptions and calculation patterns ________________________________ 67 7.2.4.1 Interview groups _____________________________________________ 67 7.2.4.2 Variable “travel costs” _________________________________________ 68 7.2.4.3 Variable “travel time“ __________________________________________ 70 8 Data, descriptive analysis________________________________________ 72 8.1 Characteristics of households ________________________________________ 72 8.2 Person characteristics ______________________________________________ 73 8.3 Trip characteristics _________________________________________________ 75 8.3.1 Modal Split _____________________________________________________ 76 8.3.2 Trip purpose and trip chaining ______________________________________ 77 9 Mode Choice in the Scenarios, descriptive analysis __________________ 80 11 9.1 Hypothetical versus realistic mode choice in the SP experiment ______________ 80 9.2 Mode shift – Trip related ____________________________________________ 81 9.3 Modal shift – Mileage related _________________________________________ 83 9.4 Selected influencing factors on mode choice_____________________________ 84 9.4.1 Mode choice and gender __________________________________________ 85 9.4.2 Mode choice and age _____________________________________________ 87 9.4.3 Mode choice and trip purpose ______________________________________ 89 9.4.4 Mode choice and trip length ________________________________________ 92 9.4.5 Mode choice and trip chaining ______________________________________ 94 9.5 Excursus: Previous research on a “New motorised two-wheeler” _____________ 97 10 Barriers and opportunities for the choice of certain modes ___________ 100 10.1 Car availability and drives for car choice _______________________________ 101 10.2 Potential of environmentally friendly modes ____________________________ 103 10.2.1 Public Transport ________________________________________________ 103 10.2.2 Car passenger _________________________________________________ 104 10.2.3 Bicycle _______________________________________________________ 105 10.3 CLEVER View ___________________________________________________ 106 10.3.1 CLEVER Assessment and User Requirements ________________________ 106 10.3.2 Market potential of CLEVER ______________________________________ 109 11 Discrete Mode Choice Model ____________________________________ 111 11.1 Modelling Assumptions ____________________________________________ 111 11.1.1 Decision maker and trip characteristics ______________________________ 111 11.1.2 Alternatives____________________________________________________ 112 11.1.3 Attributes of alternatives __________________________________________ 114 11.1.4 Theory of logit model ____________________________________________ 118 11.2 Data segmentation ________________________________________________ 120 11.3 Process of modelling ______________________________________________ 124 11.3.1 Classification of variables and hypotheses ___________________________ 124 11.3.2 Selection of variables and types of models ___________________________ 126 11.3.3 Methodological influence _________________________________________ 131 11.3.4 Editing of variables for modelling ___________________________________ 133 11.4 Discrete Choice Model and Estimation ________________________________ 136 11.4.1 Approach of modelling ___________________________________________ 136 11.4.2 Attributes of alternatives __________________________________________ 136 11.4.3 Socio-demographic characteristics _________________________________ 147 11.4.4 Attitudes and subjective motives ___________________________________ 153 12 11.4.5 All selected variables ____________________________________________ 157 11.4.6 Influence of awareness __________________________________________ 159 11.4.7 Methodological influence _________________________________________ 163 11.5 Constraints of the model ___________________________________________ 166 12 Benefits for the Environment and for Urban Traffic__________________ 168 12.1 Hazardous Air Pollutants and CO2-Emissions ___________________________ 169 12.1.1 Basics for the calculation _________________________________________ 169 12.1.2 Figures and Costs ______________________________________________ 173 12.2 Fuel consumption and running costs __________________________________ 174 12.2.1 Basics for the calculation _________________________________________ 174 12.2.2 Figures and Costs ______________________________________________ 175 12.3 Noise __________________________________________________________ 177 12.3.1 Basics for the calculation _________________________________________ 177 12.3.2 Figures and Costs ______________________________________________ 178 12.4 Road accidents __________________________________________________ 179 12.4.1 Basics for the calculation _________________________________________ 179 12.4.2 Figures and costs _______________________________________________ 181 12.5 Travel time ______________________________________________________ 183 12.5.1 Basics for the calculation _________________________________________ 183 12.5.2 Figures and costs _______________________________________________ 183 12.6 Parking infrastructure ______________________________________________ 185 12.6.1 Basics for the calculation _________________________________________ 185 12.6.2 Figures and costs _______________________________________________ 186 12.7 Welfare Losses __________________________________________________ 187 12.8 Summary of the Cost Benefit Analysis _________________________________ 187 13 Suggestions for improvements __________________________________ 191 14 Summary ____________________________________________________ 193 15 Bibliography__________________________________________________ 197 16 Annex _______________________________________________________ 204 16.1 Questionnaires ___________________________________________________ 204 16.2 Modal split in detail _______________________________________________ 211 16.3 Results of the correlation ___________________________________________ 214 16.4 Results of the discrete choice estimation _______________________________ 216 17 Curriculum vitae ______________________________________________ 253 13 1 Introduction 1.1 The Problem With the constantly increasing need for (individual) mobility, in particular in urban areas, various problems emerge affecting people concerning their environment, health and economy. In this context the consumption of urban space and energy as well as increasing exhaust and noise emissions have to be mentioned. Concerning recent discussions the care about increasing CO2 emissions in the field of transport and their reduction respectively are of special interest. In order to be able to satisfy the mobility needs in the future, solutions are required which are able to solve or at least mitigate these problems at different levels. Attractive options for public transport, walking and cycling as well as the supply of park-and- ride systems are approaches from the perspective of “soft mobility”. Still, aiming at guaranteeing sustainable transport in the future, these measures alone will not be sufficient. Therefore it is necessary to develop new concepts with environmentally compatible technology for individual urban transport to close the gap between conventional individual transport and public transport. Due to the increasing readiness of customers for the acquisition of second or third vehicles, it is expected that there will be a market for new innovative low-emission vehicles for urban transport. However, the question remains to what extent these concepts are accepted and used by the motorists and transport users in order to achieve a sustainable and measurable impact on the environment. 1.2 Objectives and aims A new low-emission vehicle for urban transport, which is developed as a prototype (CLEVER), is the centre of this research. While its design, function and advantages over conventional vehicles are well defined, its acceptance amongst car drivers and their willingness to change their mode choice in favour of the new vehicle are unclear. Beside the market potential, the measurable effects of its use are of special interest. By means of a stated preference survey (face-to-face in-depth interview technique) based on actual trips made by the interviewees in one case study city, the hypothetical mode choice as well as mobility pattern and personal motives are explored. Based on this knowledge the objectives are the following: to develop and calculate a mode choice model (or rather options of various models) comprising all relevant variables for the choice of the new vehicle instead of a conventional car, to analyse influencing factors as well as barriers for or against the choice of the innovative vehicle, to check critically the survey method particularly in terms of the plausibility of the hypothetical mode choice, to demonstrate the impacts of the changed modal split due to the use of the new vehicle, especially with regard to exhaust and CO2-emissions by means of a cost-benefit analysis (CBA). 14 Summarising the objectives above the following questions will be discussed in course of the research process: What kind of mode choice model is covering the use of the defined, new vehicle best and which influencing variables are significant? Who are the target persons for its use, how old are they, are they male or female etc.? What are the characteristics of a typical trip done with the environmentally friendly vehicle, how long is it, what kind of travel purpose is it mainly made for etc.? What kinds of motives or barriers emerge deciding to use or not to use the new vehicle? How realistic is it that one person, who states in the interview to use the vehicle, that he or she will do it in reality? Does the (hypothetical) use of the new vehicle and the changed modal split respectively have a measurable positive impact on the environment? 1.3 Setting of the research Background The thesis is based on the EU-project "CLEVER" ("Compact Low Emission Vehicle for Urban Transport", funded by the European Commission under the "Competitive and Sustainable Growth" Programme within the 5th Framework Programme), which was realised between 2002 and 2006 with the target to develop a small vehicle for clean urban transport with minimal requirements on urban space, both in traffic and parking, as well as low energy consumption and low exhaust and noise emissions. The final result of the technical work was one vehicle with full function, and additional prototypes for testing different vehicle functions. The technical project approach was accompanied by an investigation of the impacts the new vehicle concept might have on urban transport and the environment. These predicted impacts are based on a mobility survey of potential vehicle users in two European case study cities (Thessaloniki in Greece and Graz in Austria). Field of research For the thesis the data base of the CLEVER survey in Graz, was elaborated and analysed extensively considering the objectives above. The following clarification should explain the distinction between the contents of the thesis and of the CLEVER project: As the author of the thesis was the author of the CLEVER report “Benefits for urban traffic” as well, a few parts of both works are conform or have been modified and elaborated more detailed within the thesis. Completely new are the literature review, the description of the methodology and of the stated preference survey, the barriers and opportunities for the choice of modes and the discrete mode choice model, which is the focus of the thesis. All the other contents have been deepened and improved. The area of research included the city of Graz, which is the second largest city in Austria with about 226.000 residents. Only the trips by the inhabitants of the city within and leaving the city borders (internal and origin and destination trips) are considered. Trips of residents living outside the city and travelling into the surveyed area are not regarded.The period of 15 surveying and data collection lasted from November 2003 to June 2004. The reference period for the cost benefit analysis is year 2003 (rate of inflation considered). In the following the new environmentally friendly vehicle is shortly called CLEVER. Though it is presumably not to be expected that the CLEVER will be launched at the market, the results of this research may nevertheless be of interest for similar innovative vehicle concepts. 1.4 Structure of the report In a general introduction, objectives and aims of the thesis as well as the setting of research are identified (chapter 1), followed by a short literature review (chapter 2) covering a definition of small eco city cars, an abstract of the European legal framework with the focus on the reduction of CO2 emissions, demands of users and on public authorities as well as examples of extraordinary concept cars. Chapter 3 presents the background of the CLEVER project and introduces the CLEVER vehicle concept. The approach and process of research including the selection of a case study and scenarios, the survey itself and its analysis are outlined in chapter 4 ‘Methodology’. The description and quantification of the sample as well as the procedure of weighting and grossing up are provided in capter 5, following a characterisation of the Austrian case study city Graz in chapter 6. Chapter 7 comprises a detailed examination of the stated preference survey – its approach, materials and questionnaires, the design of the stated choice experiment according to the generated scenarios and assumptions and calculation pattern related to the crucial attributes of alternatives ‘travel cost’ and ‘travel time’. Chapter 8 provides the results of a descriptive analysis of the general data and findings of the survey (characteristics of households, persons and trips), while chapter 1 deals with mode choice and the change in mobility behaviour based on a descriptive analysis of the SP survey data. These results reveal the mode shift towards CLEVER according to the scenarios and to some person and trip characteristics (e.g. age, gender, trip length). In chapter 10 ‘Barriers and opportunities for the choice of certain modes’ are discussed including the availability of cars and the potential of environmentally friendly modes (PT, car passenger, bicycle). Additionally, the view on and assessment of CLEVER and its market potential are covered. Chapter 11 ‘Discrete Mode Choice Model’ is the core of the thesis. Beside a detailed description of modelling assumptions and approach and the selection of variables, the focus is on the results of the discrete choice estimation related to different types of logit models distinguished according to the entering variables (e.g. attributes of alternatives, sociodemographic characteristics), detecting the parameters influencing the choice of CLEVER. In addition, the influence of awareness and of some methodological factors on the hypothetical mode choice is addressed separately. Chapter 12 comprises the results of the Cost Benefit Analysis of the case study city Graz, examining the benefits for urban traffic by the use of CLEVER. Chapter 13 ‘Suggestions for improvements’ deals with the question about the learning effect and provides suggestions for future research on this topic, while chapter 1 ‘ 16 Summary’ identifies the main findings of research, emphasising the results of the mode choice model in connection with the SP survey and on benefits for the environment by use of the environmentally friendly CLEVER. 17 2 Literature review on the use of small eco city cars In search of a definition for “small eco city cars” and the requirements for their use, various issues from the literature have been tackled. At first, a classification of vehicles related to their size is combined with the type approval of passenger cars and motorcycles according to the directives of the European Union. As the discussion about eco cars is generally centered on the question of meeting the target of CO2 reduction, the European legal framework as well as the role of the automotive industry is considered. Well-engineered technologies and the implementation of alternative fuels are two approaches pursued by manufacturers to meet the CO2 commitment. Regarding this fact the development of the stock of cars according to their engines and fuel and simultaneously the increase of CO2 and energy consumption is looked at. Last but not least the drivers’ demands and the role of public authorithies, who build up the frame for the use and success of small eco city cars, are examined. A brief view on innovative (concept) cars will conclude the literature review. 2.1 Classification of vehicles The European Commission subdivided the passenger car market on the basis of a number of objective criteria like engine size or length of cars in several segments which could constitute distinct product markets [Commission of the European Communities (1999)]. The narrowest segmentation previously used by the Commission is the Euro Car segment provided in the following table. Beside this, the classification according to the Euro NCAP as well as the British and the American grading are compared and examples of makes are given (Table 2-1). The British grading explicitly uses “city car” as a class, which is comprised under the Euro car segment “mini cars”. A city car is a small automobile intended for use in urban areas. Unlike microcars, a city car can reach highway speeds, has more capacity and is safer in mixed traffic in terms of occupant protection. In Japan city cars are called kei cars. Kei cars have to meet strict size and engine requirements: engines have a maximum displacement of 660 cc and the car’s length must be less than 3.400 mm. The microcar is set on the margin between car and motorcycle with engines less than 1,0 litre, typically seats only two passengers, and is sometimes uncommon in construction. Some micro-cars are threewheelers, while the majority has four wheels. A descendant of the microcar is the modern Smart Fortwo. Regarding the size both types of cars summarized under “mini cars” meet the requirements of a small city car, while the type of engine determines their environmental friendliness [http://wapedia.mobi/en/Car_classification]. 18 Table 2-1: Classification of passenger cars [http://wapedia.mobi/en/Car_classification] Classification of passenger cars according to … Euro NCAP1) Euro NCAP1) 1997 - 2009 Euro Car Segment2) British grading Passenger car Supermini A : mini cars Micro car, bubble microcar car Isetta, Smart Fortwo City car Fiat 500, Toyota iQ Passenger car American grading Subcompact car Examples B : small cars Supermini Ford Fiesta, VW Polo, Opel Corsa Small family car C : medium cars Small family car Compact car Ford Focus, VW Golf, Toyota Auris Large family car D : large cars Large family car Mid-size car Ford Mondeo, VW Passat Compact executive cars Entry level luxury Audi A4, BMW 3 cars Series Executive car E : executive cars Executive car Full-size car, mid-size luxury car Audi A6, BMW 5 Series – F : luxury cars Luxury car Full-size luxury car Audi A8, Mercedes Sclass Roadster – S : sport coupes Sports car, super Sports car, super Porsche 911, car, convertible, car, convertible, Jaguar K, Ferrari roadster roadster 612 Multi purpose car Small and large M : multi purpose Leisure activity multi purpose car cars vehicle; mini, compact, large MPV Compact minivan, minivan Off-roader Small and large off-roader Mini, compact, Mitsubishi Pajero mid-size, full-size iQ, BMW X3, VW SUV Touareg J : sport utility cars Mini, compact, coupe, large 4x4 Skoda Roomster, Opel Zafira, Ford Galaxy 1) European New Car Assessment Programme 2) European Commission Classification [Regulation (EEC) No 4064/89] According to type approval (homologation) of new vehicles in Europe, which has to meet the regulations and directives of the European Union, small city cars can either be appoved as passenger cars (M1) or as motorcycles (L). These two different vehicle classes L (motorcycle) and M (passenger cars) are defined in the EC directives 2002/24/EC and 2007/46/EC (amending 70/156/EEC) (Table 2-2 and Table 2-3). 19 Table 2-2: Vehicle classes L (motorcycles) according to the EC directive 2002/24/EC http://europa.eu.int/eur-lex/ Class Propulsion L (2002/24/EC) Power [kW] Capacity [ccm] Type L1e/L2e Light motorcycle 2- or 3-wheeled vehicle – max. 50 IC engine max. 4 – L3e/L4e Motorcycle, 2-wheeled vehicle with or without side car – L5e 3-wheeled vehicle, symmetrical arranged wheels L6e 4-wheeled vehicle, treated like a light motorcycle L7e 4-wheeled vehicle, treated like a 3-wheeled vehicle vmax Kerb weight [km/h] [kg] and max. 45 – Electric powered and max. 45 – > 50 IC engine and/or > 45 – – > 50 IC engine and/or > 45 – – – Electric powered and max. 45 < 350 – max. 50 Extraneous ignition and max. 45 < 350 max. 4 – – and max. 45 < 350 max. 15 – – and max. 400 max. 15 – – and max. 5504) 1) 2) 3) 1) IC engine = internal combustion engine 2) Vehicles over 45 km/h could be also electric powered. 3) Weight of batteries in kerb weight excluded. 4) Vehicles for transport of goods. Table 2-3: Vehicle classes M (passenger cars) according to the EC directive 2007/46/EC [http://europa.eu.int/eur-lex/] Class Propulsion vmax Kerb weight M (2007/46/EC) Power [kW] Capacity [ccm] Type [km/h] [kg] M1 max. eight seats (excl. driver) – – – – max. 3.500 M2 more than eight seats (excl. driver) – – – – max. 5.000 M3 more than eight seats (excl.driver) – – – – min. 5.000 20 2.2 Reducing CO2 emissions 2.2.1 European Transport Policy 2.2.1.1 Green Paper by the European Commission “The Green Paper “Towards a new culture for urban mobility” aims to initiate the debate on issues specially related to urban transport and to elicit applicable solutions at a European level. A central idea of the forthcoming strategy is the need to integrate the various urban mobility policies in a single approach. The Commission proposes to encourage the emergence of a real “urban mobility culture” integrating economic development, accessibility and improvement to quality of life. For this purpose the Green Paper identifies five challenges [European Commission (2007)]: Towards free-flowing towns and cities, aiming at combating congestion, Towards greener towns and cities, tackling the main environmental issues (CO2, air pollutant emissions and noise), Towards smarter urban transport, promoting the development and use of Intelligent Transport Systems, Towards accessible urban transport, alleviating accessibility to mobility for all people living in cities, Towards safe and secure urban transport, covering European road safety policies.” In view of this background the launch and promotion of small eco city cars is a promising policy in which all five challenges are addressed. 2.2.1.2 CO2 legislation As the focus of small eco city cars is above all on contributing to a reduction of CO2 emissions in towns and cities, a brief look is taken at the most relevant European regulation. “Regulation (EC) No 443/2009 of the European Parliament and of the council is setting emission performance standards for new passenger cars as part of the Community’s integrated approach to reduce CO2 emissions form light-duty vehicles (2) with the objective of a 30% reductions of greenhouse gases by developed countries and a 20% reduction of greenhouse gas emissions by the European Union by 2020 (compared to 1990 levels). (3) One of the implications of those commitments is that all Member States will need to reduce significantly emissions from passenger cars, as road transport is the second largest greenhouse gas emitting sector in the Union and its emissions continue to rise. (6) The Commission adopted a Community Strategy for reducing CO2 emissions from cars in 1995. The strategy was based on three pillars: voluntary commitments from the car industry to cut emissions (compare chapter 2.2.2), improvements in consumer information and the promotion of fuel efficient cars by means of fiscal measures. 21 (11) Appropriate funding should be ensured in the general budget of the European Union to promote the development of technologies intended to reduce radically CO2 emissions from road vehicles. (13) The aim of this Regulation is to create incentives for the car industry to invest in new technologies. It actively promotes eco-innovation and takes into account future technological developments. The development of innovative propulsion technologies should particularly be promoted, as they result in significantly lower emissions than traditional passenger cars. (Article 1) This regulation establishes CO2 emission performance requirements for new passenger cars (applying to motor vehicles of category M1) in order to ensure the proper functioning of the internal market and to achieve the overall objective of the European Communiy of 120 g CO2/km as average emissions for the new car fleet. It sets the average CO2 emissions for new passenger cars at 130 g CO2/km, by means of improvement in vehicle motor technology, as measures in accordance with Regulation (EC) No 715/2007 and its implementing measures and innovative technologies and by an increased use of sustainable biofuels resulting in an additional 10 g CO2 reduction. From 2020 onwards, this Regulation sets a target of 95 g CO2/km as average emissions for the new car fleet [EUROPEAN COMMISSION (2009)].” 2.2.2 Position of the automobile industry According to the Commission’s Regulation (EC) No 443/2009 presented above, “(7) in 1998, the European Automobile Manufacturers’ Association (ACEA) adopted a commitment to reduce average emissions from new cars sold to 140 g CO2/km, which is equivalent to 6 l/100 km, by 2008 and in 1999, the Japanese Automobile Manufacturers’ Association (JAMA) and the Korean Automobile Manufacturers’ Association (KAMA) followed with the same commitment to be fulfilled by 2009. … (19) Manufacturers should have flexibility to decide how to meet their targets under this Regulation and should be allowed to average emissions over their new car fleet rather than having to respect CO2 targets for each individual car.“ Figure 2-1: EU Trends of ACEA’s Fleet in Specific Average Emissions of CO2 [Commission of the European Communities (2002)] 22 Within this commitment the average specific CO2 emissions of all new registered vehicles in Europe (petrol and diesel) has already been reduced from 185 g/km in 1995 to 169 g/km in 2000 and 164 g/km in 2001. Thus, it appears that from 1995 to 2001 the average CO2 emissions were cut by 11,4%; this represents an average reduction of 1,9% per year (Figure 2-1) [CLEVER D1 (2002)]. Since 1995 CO2 emissions have fallen by close to 20%, with average CO2 emissions of around 154 g/km in 2008 [ACEA (2010)]. To achieve the targeted CO2 reductions “the European automotive industry has identified multiple categories for eco-innovative car technologies: systems & components, running resistance, well-to-wheel efficiency (use of certain alternative fuels), smart navigation and driver information. All categories contain numerous technology applications, from adaptive cruise-control and super efficient LED lights to robotised gearboxes and the storage and reuse of heat [ACEA (2010)]” “CO2 targets for passenger cars are defined according to the utility of the cars on a linear basis. To describe this utility, mass is an appropriate parameter which provides a correlation with present emissions [EUROPEAN COMMISSION (2009): (12)].” In 2014, there will be an evaluation of the average mass (weight) development of cars over the previous three years; with a possible adjustment of the CO2 targets implemented in 2016. There will be a review every three years after. Manufacturers that exceed their target by more than 3 grams have to pay penalties, which are 95 Euro per excess gramme [ACEA (2010)]. “Apart from the supplying of CO2-cutting technologies, which the industry is doing, informing consumers about production innovation, building awareness and encouraging consumer acceptance of new models will all be essential to meet fuel efficiency standards. The auto industry is ready to meet the challenging legislation for passenger cars. Nevertheless only an integrated approach, covering the effort of vehicle makers, fuel companies, governments, transport operators and consumers, is necessary to reduce CO2 emissions efficiently [ACEA (2010)].” 2.2.3 Alternative fuels and their role in the growing vehicle fleet To tackle the problems which are rising with an increasing energy demand for individual mobility (growing demand on fossil fuels, increasing greenhouse gas emissions, dependency on imports of fossil fuels etc.), alternative fuels like bioethanol, biogas and biodiesel, hydrogen from renewable resources, synthetic fuel and electricity gain in importance and are dicussed and supported around the world. The focus of the Austrian research project ALTANKRA [HAAS R. et al. (2008)] is on testing the emissions as well as the economic feasibility of these products and gives a foresight of the development of individual transport in view of these influences: Fossil fuels comprise petrol, diesel and natural gas as well as synthetic fuels, which are based on natural gas and called gas-to-liquids (GTL). Hydrogen gained from natural gas and applied in fuel cell cars or in combustion engines, as well as electric drive (run by the Austrian electricity-mix) are also among the fossil chains. Renewable energy sources are rape, sunflower seeds and waste oil for biodiesel and corn, wheat, sugar beets, wood and straw for the production of bioethanol. In addition, biogas is considered (produced out of liquid manure or a mix of energy crops), which can be used processed to CNG in natural gas dedicated vehicles, which on the other hand can also be run by synthetic natural gas (SNG) gained from wood. Biofules are defined under the European directive 2003/30/EG. 23 The results of testing the greenhouse gas emissions (split to CO2, CH4 and N2O) of vehicles with different propulsion systems (related to car kilometers travelled) in ALTANKRA are shown in the following figures (Figure 2-2 and Figure 2-3). PKW Benzin vkm … car petrol, IC (internal combustion) engine PKW Strommix E-Motor … car electricity mix, electric engine PKW Benzin hyb … car petrol, hybrid PKW H2 Ergdgas vkm … car hydrogen natural gas, IC engine PKW Diesel vkm … car diesel IC, engine PKW H2 Ergdgas hyb … car hydrogen natural gas, hybrid PKW Diesel hyb … car diesel, hybrid PKW H2 Ergdgas bz … car hydrogen natural gas, fuel cell PKW Erdgas vkm … car natural gas, IC engine PKW GTL vkm … car gas-to-liquid, IC engine PKW Erdgas hyb … car natural gas, hybrid PKW GTL hyb … car gas-to-liquid, hybrid Figure 2-2: left: Greenhouse gas emissions ACTUAL (2007) in g CO2-equivalent/car-km for fossil fuels [HAAS R. et al. (2008)] Figure 2-3: right: Greenhouse gas emissions FUTURE (2050) in g CO2-equivalent/car-km for fossil fuels [HAAS R. et al. (2008)] The highest greenhouse gas emissions in the actual scenario (referring to 2007) are calculated for hydrogen (out of natural gas) running a car with combustion engine or hybrid propulsion respectively, while the lowest figure appears for fuel cell and electric cars. The results of the calculation of the greenhouse gas emissions for the future scenario (referring to 2050) are quite similar with a reduction from 10% to 25% and 65% for electric cars compared to the base scenario [HAAS R. et al. (2008)]. For comparison, cars run by liquid (biodiesel, bioethanol) or gaseous (biogas, hydrogen) fuels from renewable resources emit approximated 20% to 65% less greenhouse gases than fossil fuels. Having a separate look at fossil fuels shows that diesel cars emit less greenhouse gases than natural gas and petrol driven vehicles. On the other hand, diesel cars emit far more air pollutants (sooty particles and NOx) than petrol cars [HAAS R. et al. (2008)]. Although the lower fuel consumption of diesel cars leads to less greenhouse gas emissions than emitted by petrol cars, this advantage is compensated by an increasing number of diesel cars (Table 2-4). While in Austria more petrol than diesel cars were registered in the year 2004, this ratio turned around in 2009 and at the same time alternative engines (hybrid, electric drive, CNG) slowly emerged. 24 Table 2-4: Stock of private passenger cars in Austria according to fuel 2004 and 2009 [STATISTIK AUSTRIA 2005 and 2010] 2004 2009 Fuel Number Share in % Number Share in % Petrol 2.087.180 50,79% 1.972.352 45,24% Diesel 1) 49,20% 2.381.906 54,63% 128 0,00% 223 0,01% 2.021.821 Electric drive Liquid petrol gas (LPG) – – 1 0,00% Compressed natural gas (CNG) – – 1.105 0,03% Bivalent drive Petrol / LPG – – 56 0,00% Bivalent drive Petrol / CNG – – 742 0,02% Hybrid Petrol / Electric drive – – 3.559 0,08% 4.359.944 100% Total 1) 4.109.129 100% including CNG and LPG The development of the vehicle stock around the world is expected to rise till the year 2050 up to 2 milliard vehicles according to IEA (2007) (Figure 2-4). Hybrid vehicles will replace diesel and petrol cars, fuel cell-hydrogen vehicles will gain in importance, while the number of natural gas driven vehicles will decrease. Although biogenous fuels will become more important, they will play only a marginal role in the global view. Wasserstoff BZ … Hydrogen fuel cell Diesel-Motor … Diesel engine Figure 2-4: Wasserstoff VKM … Hydrogen IC engine Benzin Hybrid … Petrol hybrid Erdgas-Fahrzeug … Natural gas vehicle Otto-Motor … Petrol IC engine Development of the worldwide vehicle stock [IEA (2007)] Despite the negative forecast by IEA (2007) concerning the use of natural gas, the European Union aims at a share of 10% natural gas vehicles on road traffic till 2020, arguing that those engines nearly emit no particulate matter and less CO2 and NOx than other fossil fuels [CHRISTMANN U. (2007)]. Within the framework of the project CNG 600-Mono (2007) a tank system for CNG vehicles has been developed with the aim to enlarge the range of a 25 (monovalent) CNG vehicle up to 600 km with one tank and hence raising acceptance for those vehicles among the customers. Raising awareness and developing the market for gas driven vehicles is also the focus of the project MADEGASCAR, with the overall goal to increase the number of energy efficient and alternative fuelled vehicles in European countries [MADEGASCAR (2010)]. Looking ahead, it is expected that energy consumption as well as greenhouse gas emissions will not increase in the same way the vehicle stock does but to a lesser extent (or even decrease) due to technical progress regarding vehicles and fuels and due to the implementation of energy and environmental policies [HAAS R. et al. (2008)]. 2.3 Demands on small eco city cars Small eco city cars represent a special type of urban mode with different demands made by manufacturers and users, while politics provide the frame for their use. At best, the approach and view of these three involved parties overlaps for a successful launch and promising use. Small eco city cars are characterized by low emissions, low energy consumption and minimal area requirements in traffic and parking. They are small, light, silent, safe and environmentally friendly. 2.3.1 Manufacturers’ approach The position of the automobile manufacturers is a complex one, as they aim to meet the demands of quite different groups of clients to realise profit under regulatory requirements (compare previous chapter 2.2.2). Although small eco city cars are only one segment in the product range they offer, a special requirement profile has to be considered. CANZLER (1999) queries this approach as he says that “there are four essentials in the engineers’ product specifications which establish the concept of a universal car: It has to provide a perfect acceleration and maximum speed, offers space for at least four persons plus luggage and has to guarantee a range of 500 km with one tank contents. This profile is strongly linked to the qualities of combustion engines. … As soon as one of these criteria is missing alternative concepts have few chances to be successfully launched on the market.” It is noticeable that even small cars are designed according to these specifications with the aim to supply the market with second and third cars. These cars are very well equipped and motorized and provide any comfort of a middle-class car [CANZLER (1999)]. Safety is above all a feature with high attention, as city cars are generally very light and have disadvantages in case of collision. The dimensions as well as the weight of city cars are minimized to attain lower energy consumption and fewer emissions without reducing comfort and safety for the car occupant [SCHINDLER (1994)]. Nearly each of the leading automobile manufacturers puts effort in the development and design of small eco city cars (compare chapter 2.4). In this process, marketing and sales promotion are essential to attract interest for a not yet available product. Innovative concepts have to be promoted as lifestyle products and not as small cars associated with narrowness and discomfort [CANZLER (1999)]. The image campaign of the SMART for example focused successfully on a special identity for a young and modern urban mobility. 26 2.3.2 Users‘ requirements The success of an innovative car concept is primarily dependent on the acceptance of the users. To get to know the needs and demands of transport users, patterns of vehicle use and studies of buying motives are investigated. Patterns of vehicle use can be distinguished according to [HAUTZINGER H. (1994)]: − Frequency and purpose of use, − Trip length and daily driving performance, − Number of passengers (occupancy rate) and − Transportation of goods. These indicators are dependent on socio-demographic characteristics, characteristics of the household structure, number and type of vehicles in the household etc. Generally the patterns of car use in private households in urban areas can be characterized by [HAUTZINGER H. (1994)]: − Low frequency of use, − Long period of parking, − Short trip length, − Low daily driving performance and − Low occupancy rate. In most cases only a few of the four or five seats in a passenger car are occupied. In a traffic count on an urban motorway in Vienna, Austria an occupancy rate of 1,18 was acquired on a Monday morning, confirming the thesis that especially commuting trips have an extremely low occupancy rate [SAMMER G. et al. (2000)]. To demonstrate the share of daily trip lengths the example of the city of Graz, Austria is used: A mobility survey among the inhabitants of Graz showed that 21% of all trips are shorter than 2,9 km, 53% are shorter than 5,7 km and more than three quarters are shorter than 10 km. The car is predominatly used for shopping and errands, leisure and commuting trips [STADT GRAZ (2008)]. Based on these patterns of use, the requirements for a city car concerning transport capacity and range might be lower than on a universal car. Nevertheless, if a car is the only one in a household it is normally used as a universal car – for shopping as well as for holiday trips. Under these circumstances a small alternative vehicle has less chance to be accepted, since the limits of use are easily reached with respect to the transport capacity or the range of the vehicle [MEISSNER T.; APPEL H. (1994)]. While the patterns of use have at least an objective influence on the decision for or against a certain car, the individual reasons and motives for buying are comparable or even more meaningful. They are important indicators for the estimation of the market potential and consequently for the evaluation of the benefits for transport policy and the environment. The importance of different criteria for purchasing a car has been investigated within a respresentative survey in Germany 2007 [Arbeitsgemeinschaft Verbrauchs- und Medienanalyse (2007)]. Figure 2-5 shows the results indicating that reliability and costperformance ratio are the most important attributes, followed by convincing technology and design. It is noticeable that environmentally friendliness is not listed or evidently of less importance, although automakers concede that “drivers have, more recently, started to value 27 fuel-efficient technologies alongside the more traditional favourites such as comfort, safety and design [ACEA (2010)].” Criteria for car purchase 95% Reliability 94% Cost-performance ratio 82% Convincing technology Styling, design 69% Family friendliness 66% 58% Image not specified 0% 2% 20% 40% 60% 80% 100% Share of respondents Germany; respondents 14 years and older; Ifak Institute © Statista.org 2008; VuMa 08 Figure 2-5: Importance of different criteria for purchasing a car [Arbeitsgemeinschaft Verbrauchs- und Medienanalyse (2007)] Amongst others based on the perceptions above a qualitative choice model for the purchase of alternative vehicles was made by PFAFFENBICHLER P. C. et al. (2009): “It reveals costs, including investment costs (purchase or leasing of a vehicle) as well as running costs, to be the most important attributes for the decision to buy a car powered by an alternative engine. The more expensive an alternative vehicle is, regarding investment and running costs, the less is the probability that this option is chosen. Another crucial decision criterion is the reliability of alternative engines, comprising the suspectibility for a breakdown, the repair frequency as well as a reliable supply of fuel and garages. The driving range of a vehicle is also relevant especially for electric cars. The ranges with one barrery charge/tank contents as well as the concentration of petrol stations are important requirements for potential users. Further attributes influencing the purchase decision are the image of the alternative technology, its maximum speed and incentives favouring the alternative vehicle in urban areas [PFAFFENBICHLER P. C. et al. (2009)]”. A stated preference experiment testing the purchase decision of vehicles with alternative engines was carried out by the University of Leeds, Institute for Studies [BATLEY R. P., TONER J. P. (2003)]: “The respondents could choose between three hypothetical vehicle types: 1) Car A: corresponds to a conventional passenger car powered by diesel or petrol. 2) Car C: corresponds to a vehicle with alternative engine, which would be launched in the near future. 3) Car B: is a compromise between car A and car C. It could be either a very efficient vehicle with a conventional combustion engine or a prospective car with an alternative engine with characteristics coming very close to a conventional passenger car.” 28 “The vehicles were specified by the following attributes: − Purchase costs, − Running costs, − Driving range with one tank contents or battery charge, − Duration of refuelling or recharging, − Maximum speed, − Acceleration 0-100 kph, − Resale value after three years or 60.000 kilometers, − Emissions in percentage of emissions of a car with combustion engine year of manufacture 2000.” The estimation of a multinomial Logit model (MNL) testing the influences of the presented attributes on the hypothetical purchase decision resulted in [BATLEY R. P., TONER J. P. (2003)]: • “A significant influence of the attributes purchase costs, running costs, driving range and duration of refuelling on a significance level of 1%, • A significant influence of the attribute emissions on a significance level of 5%, • No significant influence oft the attributes maximum speed, acceleration and resale value.” 2.3.3 Policy actions “Many countries in Europe provide a variety of incentives to support clean fuel vehicles, including NGV (natural gas vehicles). These range from tax incentives, purchase incentives, special access at train stations and airports for clean fuel taxi, exemption from congestion charges and many others [European Natural Gas vehicle Association (2007)].” Policy actions and the political influence on framing the operational space for car use gain in importance to confine urban problems induced by the growing vehicle fleet (CO2 emissions, polluting emissions, noise, road safety, land consumption etc.). Public authorities have various options to take action in the promotion of eco city cars and alternative engines. Table 2-5 lists some examples structured according to users’ decision criteria favoring that type of vehicles [PFAFFENBICHLER P.C. et al. (2009)]. 29 Table 2-5: Options favouring the use of eco city cars and/or alternative engines according to decision criteria and responsibility Decision criteria Options and measures How? Responsibility Investment costs Standard consumption tax 1) direct Public authorities Value added tax (VAT) direct Public authorities Investment grant direct Public authorities Petroleum tax direct Public authorities Value added tax (VAT) direct Public authorities Motor based insurance tax direct Public authorities Promotion to build up a net of filling stations and garages indirect Public authorities Training of skilled technical staff indirect Public authorities Improvement of technological development direct Automobile industry Promotion to build up a net of filling stations indirect Public authorities Promotion of technological development indirect Public authorities Improvement of technological development direct Automobile industry Speed maximum and acceleration Improvement of technological development direct Automobile industry Image Image campaign direct Public authorities Image campaign, promotion direct Automobile industry Exemption from congestion charges direct Public authorities Exemption form restricted areas direct Public authorities Cheap parking rates direct Public authorities Running costs Reliability Driving range Operational frame 1) Standard consumption tax = Normverbrauchsabgabe (NOVA) An example – given that some of the suggested actions have already been implemented – is that electric cars in Austria are exempted from standard consumption tax and motor based insurance tax. Hybrid vehicles get a standard consumption tax bonus of 500 Euro [PFAFFENBICHLER P.C. et al. (2009)]. The internalisation of traffic costs (polluter pays principle) and the consideration of vehicle specific parameters (weight, length, energy consumption, emissions) for charges and taxes are further ideas to promote eco city cars in future. All these measures and policies may have a vital influence on a successful launch of compact eco city cars, as it is undoubted that these vehicles with limited options of use have actually only market opportunities benefiting from special advantages and incentives [MEISSNER, T.; APPEL H. (1994)]. 30 2.4 Vehicle concepts and concept cars Nearly each of the leading auto manufacturers offers at least one small type of car among their vehicle fleet dedicated for urban traffic. Ambitious efforts aim at designing innovative, compact, easily manoeuvrable, comfortable and safe city cars. Beside their environmental friendliness, provided by low energy consumption or alternative drives, the focus is on an extraordinary design in the majority of cases. A selection of promising concepts and city cars of the newest generation, in which CLEVER definitely queues up, is presented below. 2.4.1 Mazda Kiyora “Mazda Kiyora Kiyora (meaning 'clean and pure' in Japanese), a lightweight, next generation, urban compact concept car, has been revealed at the 2008 Paris International Motor Show (Figure 2-6). … Featuring next-generation environmental technologies, Kiyora was envisaged as a fun and cool concept for young European urbanites. … It is highly fuel efficient, with a very small CO2 footprint and high levels of safety. The car achieves this by Mazda’s lightweight strategy (with a weight about 900 kg and a length of 3.77 m) by employing an extremely rigid and lightweight carbon-fibre body structure beneath a small, aerodynamic outer skin and a spirited, small-displacement 1.3-liter direct-injection engine. … Figure 2-6 Mazda Kiyora’s exterior view and interior design © Mazda The engine is spirited as well as clean and efficient and, in combination with a compact and lightweight six-speed automatic transmission with manual shift control, it would make Mazda Kiyora powerful and cultivated, even at low engine speeds. In stop and go urban traffic conditions, Mazda's newly developed Smart Idle Stop System (SISS) would save fuel by automatically shutting down the engine when the vehicle is stationary, and achieves a quick and quiet restart for stress free driving. Emissions would be among the lowest thanks to a new catalyst that more effectively removes harmful exhaust materials. … With these technologies, the Mazda Kiyora concept would produce CO2 emission of under 90g/km (which is equivalent to approximately 3.7 l/100 km). Developing an exciting new design message with its Nagare series the Mazda Kiyora concept car is formed in the shape of a water droplet on its side, as are its two side windows. … The roof is transparent, for an open-air feeling on the inside, and has photovoltaic solar cells which provide electricity for the car's interior systems. … The visible body structure is a 31 real structural element of the car – stiff and crash-resistant. It is indicative of Mazda's approach to conduct a thorough structural analysis to solve complex issues such as safety and rigidity requirements. … A new Liquid-Skin Display IP Concept in the interior would be a simple yet very practical type of instrument panel that uses advanced touch-screen technology with tactile feedback. … Using liquid-skin display technology, the driver would be able to move information icons around with his finger and could even organise them however he wished. He could lip through menus, select settings for temperature, and even send an email. From this touchscreen display, you could also control a hard-disk drive with advanced sensors that would provide environmental information like how much fuel you used and how many grams of CO2 you released into the atmosphere on a particular day. It could also calculate how many toxins the car filtered out of the air (by means of activated carbon) and water during the same period. … The exterior of the Mazda Kiyora is blue-green and has transparent, polycarbonate doors, chosen to underscore the purity of water. … The interior is dominated by the body shell, which is visible, like an inner skeleton, and supports the water theme with its wavy, lowing shapes, while functioning as a true body structure. … Its lexible interior can be used as a two-seater with boot, or as a 2+2 seating arrangement. … The idea for this car was born from research that identified market opportunities to address future unmet customer needs with innovative concepts and ideas. Analysing the small city car segment in Europe, the focus was on the urban costumer with a post-modern lifestyle. Exterior styling, compact size, manoeuvrability and flexibility were just as important to European urbanities, using their cars in the city of the future, as high fuel efficiency, safety and environmentally friendliness.” With the Kioyra concept of a small, energy efficient and environmentally friendly city car with an extraordinary design these demands were met by far. [Source: Mazda 2010; http://www.mazda.at/aboutmazda/concept_cars/kiyora/ (access November 2010) and http://www.conceptcarz.com/vehicle/z15695/Mazda-KiyoraConcept.aspx (access November 2010)] 2.4.2 Renault Twizy Z.E. “Renault Twizy Z.E. Concept (Figure 2-7) is an innovative response to the challenge of urban mobility. With its four-wheel chassis, Twizy Z.E. Concept offers the driver and passenger – seated one behind the other – an allelectric means of transport which produces no CO2 emissions. … The ultra-compact dimensions (2.30m in length, and just 1.13m wide) ensure that it is nimble enough for urban use. With a turning circle of just three metres and a footprint barely larger than that of a scooter, the vehicle is easy to park in town. … Twizy Z.E. Concept provides optimum levels of safety. A deformable structure protects occupants in the event of a frontal impact while lateral reinforcement bars provide protection in the case of an impact from the side. The retention systems include a four-point harness for the front seat and a three-point seatbelt at the rear, plus a driver airbag and two lateral airbags. … The front and rear ends are equipped with a luminous matrix display, the honeycomb-shaped diodes of which allow the driver to interrelate with his or her immediate surroundings. In addition to serving as headlights and rear lights, these diodes can also produce 'smileys' which change expression as a function of the message the driver wishes to communicate. Inside, the cabin is brightly lit thanks to the bodywork's extensive glazed 32 surface. The blue and white colour scheme creates a soothing atmosphere which isolates the passengers from the stress of urban activity. Figure 2-7: Renault Twizy Z.E. © Renault Twizy Z.E. Concept is powered by a 15kW (20hp) electric motor. This develops 70Nm of torque, and combines comfort with responsive performance at all engine speeds. It can accelerate at a similar rate to a 125cc bike with a top speed of 75kph. The energy available onboard Twizy Z.E. Concept serves just one purpose – mobility. The open chassis calls for neither heating nor climate control, both of which consume a significant amount of energy. This, coupled with the vehicle's low weight (just 420kg, complete with batteries), contributes directly to its range which can reach 100km. The lithium-ion batteries for Twizy Z.E. Concept are located beneath the two seats. They are charged by means of an extendible cable located behind the Renault logo at the front. This cable can be plugged into a 220V 10A or 16A domestic socket, and will fully charge the batteries in just three and a half hours. Twizy Z.E. Concept is the forerunner of one of the all-electric mobility solutions that Renault will introduce from 2011.” [Source: Renault 2010; http://www.conceptcarz.com/vehicle/z17444/Renault-Twizy-ZEConcept.aspx, (access: November 2010)] 2.4.3 GM EN-V Concept “General Motors and its strategic partner, Shanghai Automotive Industry Corp. Group (SAIC), share a common vision for addressing the need for personal mobility through a radical change in personal urban transportation. They are exploring several solutions for tomorrow’s drivers. Among the most promising is a new vehicle form called EN-V (Figure 2-8), which was presented at the World Expo 2010 in Shanghai. 33 Figure 2-8: City cars of tomorrow ©GM EN-V Concept EN-V, which is short for Electric Networked-Vehicle, … is a two-seat electric vehicle that was designed to alleviate concerns surrounding traffic congestion, parking availability, air quality and affordability for tomorrow’s cities. Three EN-V models were unveiled in Shanghai, representing three different characteristics that emphasize the enjoyable nature of future transportation: Jiao (Pride), Miao (Magic) and Xiao (Laugh). … EN-V’s platform has evolved from the platform of the Personal Urban Mobility and Accessibility (P.U.M.A.) prototype that was developed by Segway and debuted in April 2009. Segway has worked collaboratively with GM to develop and deliver multiple copies of the drivetrain platform that seamlessly connect to and power the various EN-Vs. EN-V is propelled by electric motors in each of its two driving-mode wheels. Dynamic stabilization technology empowers EN-V, giving it the unique ability to carry two passengers and light cargo in a footprint that’s about a third of a traditional vehicle. … Power for the motors is provided by lithium-ion batteries that produce zero emissions. Recharging can occur from a conventional wall outlet using standard household power, allowing EN-V to travel at least 40 kilometers on a single charge. … By combining the Global Positioning System (GPS) with vehicle-to-vehicle communications and distance-sensing technologies, the EN-V concept can be driven both manually and autonomously and offers mobility to people who could not otherwise operate a vehicle. Its autonomous operating capability offers the promise of reducing traffic congestion by allowing EN-V to automatically select the fastest route based on real-time traffic information. … The ability to communicate with other vehicles and with the infrastructure could dramatically reduce the number of vehicle accidents. Using vehicle-based sensor and camera systems, EN-V can “sense” what’s around it, allowing the vehicle to react quickly to obstacles or changes in driving conditions. For example, if a pedestrian steps out in front of the vehicle, EN-V will decelerate to a slower and safer speed and stop sooner than today’s vehicles. … EN-V has been designed for the speed and range of today’s urban drivers. It weighs less than 500 kilograms and is about 1.5 meters in length. By comparison, today’s typical automobile weighs more than 1,500 kilograms and is three times as long. In addition, today’s automobiles require more than 10 square meters of parking space and are parked more than 90 percent of the time. EN-V’s smaller size and greater maneuverability mean the same parking lot can accommodate five times as many EN-Vs as typical automobiles. While EN-V leads the way in terms of efficiency and technology, it also sets a new benchmark for vehicle design. … The body and canopy of EN-V are constructed from carbon fiber, custom-tinted Lexan and acrylic, materials that are more commonly used in race cars, military airplanes and spacecraft because of their strength and lightweight characteristics. … 34 EN-V’s compact size makes it ideal for use in densely populated cities thanks to its use of advanced safety and propulsion technologies.” [Source: GENERAL MOTORS (2010), http://media.gm.com/content/media/us/en/news/news_detail.brand_gm.html/content/Pages/n ews/us/en/2010/Mar/0324_env (access November 2010)] 2.4.4 MIT CityCar “Researchers at MIT under the guideance of William J. Mitchell are building a prototype of a lightweight electric vehicle that can be cheaply mass-produced, rented by commuters under a shared-use business model, and folded and stacked like grocery carts at subway stations or other central sites. It's called the City Car (Figure 2-9), and …is designed to meet the demand for enclosed personal mobility – with weather protection, climate control and comfort, secure storage, and crash protection – in the cleanest and most economical way possible. It weighs less than 500 kg, parks in much less space than a Smart Car, and is expected to get the equivalent of 150 to 200 miles per gallon of gasoline [equivalent to 1.2 l gasoline/100 km]. Since it is battery-electric, it produces no tailpipe emissions. … Figure 2-9: MIT CityCar © MIT William J. Mitchell & Smart Cities Design team Lithium-ion batteries are housed in the floor of the CityCar, which provides a large amount of space, keeps the center of mass low, and facilitates cooling. Recharging can be accomplished with inexpensive home charging units, and with units installed at workplace parking structures. More interestingly, it seems feasible to provide automatic recharging in parking spaces, much like the recharging of electric toothbrushes in their holders. … The architecture of the CityCar is radical. It does not have a central engine and traditional power train, but is powered by four in-wheel electric motors. Each wheel unit contains drive motor (which also enables regenerative braking), steering, and suspension, and is independently digitally controlled. This enables maneuvers like spinning on its own axis (an O-turn instead of a U-turn), moving sideways into parallel parking spaces, and lane changes while facing straight ahead. … Shifting drive to the corners in this way enables the CityCar to fold to minimize parking footprint, and to provide front ingress and egress (since there is no engine in the way). This dramatically changes its relationship to streets and cities. It can park nose-in to the curb in far less than the width of a traditional parking bay, and it can park 35 at very high densities. It is possible to park three or four CityCars in the length of a traditional parking bay. … The front compartment of a CityCar accommodates passengers and the rear compartment provides generous storage for baggage, groceries, and so on. When a CityCar folds, the baggage compartment remains level and low for easy access. … CityCars are designed for intra-urban trips, which are fairly short between recharge opportunities. This fits them gracefully to the capabilities of battery technologies that are presently available or likely to be available in the near future. They are not designed for intercity travel, for which different technologies are more appropriate. … [MIT Smart Cities Group: http://cities.media.mit.edu/projects/citycar.html (access November 2010)].” 36 3 CLEVER – Compact Low Emission Vehicle for Urban Transport 3.1 CLEVER Project The objective of the project “Compact Low Emission Vehicle for Urban Transport” (CLEVER) – funded by the European Commission under the ‘Competitive and Sustainable Growth Programme’ of the Fifth Framework Programme, started 2002 and finished 2006 – was the development of a small vehicle for clean urban transport with minimal requirements on urban space, both in traffic and parking, as well as low energy consumption and low exhaust and noise emissions. To improve the usage of alternative energies for the propulsion of vehicles, the development of a new storage and refuelling technology had been developed. Concretely, with the novel technology for refuelling of natural gas, the market share of natural gas vehicles is expected to increase. A high level of pedestrian as well as occupant protection was crucial for a thorough safety concept. An innovative design on the one hand and benefits for the user by means of low energy costs on the other hand should guarantee the acceptance of the new vehicle among potential users. With the innovative vehicle concept, new utilisation segments for the use of vehicles with low emission technology are expected to be opened aiming at improving urban transport and mitigating the negative environmental impacts from increased mobility [CLEVER 2003]. The improvement of individual urban transport in Europe through the development of a new vehicle concept required co-operation between technical and transport organisational science to obtain the required wide acceptance for the new vehicle. Nine partners from four European countries (Austria, Germany, France and UK) built up the CLEVER consortium. The development of the vehicle was divided into the following task packages: styling, safety concept, frame, body panel, propulsion system, transmission, chassis as well as construction of prototypes and vehicle testing. The final result of the technical work was one vehicle with full function and additional prototypes for testing of different vehicle functions (e.g. passive safety). The technical project approach was accompanied by an investigation of impacts the new vehicle might have on urban transport. Predicted impacts were based on a survey of potential vehicle users in two European case study cities (Graz/Austria, Thessaloniki/ Greece). 3.2 CLEVER Vehicle concept The most noticeable characteristic of the CLEVER vehicle is that it is a tilting three-wheeled vehicle with a remarkable design (Figure 3-1) powered by compressed natural gas (CNG). “It offers room for two occupants sitting in a tandem arrangement (Figure 3-2). The external dimensions are about 3 m length, 1 m width and 1,4 m height. The aluminium space frame cabin together with the full lining protects the occupants against weather conditions and offers a suitable passenger compartment stiff enough to withstand normal accident conditions. Due to the CNG engine the energy consumption is less than 2,4 l gasoline equivalent per 100 km. A special refuelling system allows using CLEVER in areas with insufficient natural gas infrastructure [CLEVER 2003]”. 37 Figure 3-1: Front side of the three-wheeled CLEVER (© Naumann Design) Figure 3-2: Interior of CLEVER – offering room for two occupants (© Naumann Design) CLEVER CNG Engine “The designated 213 cc one cylinder CNG engine accelerates the CLEVER vehicle to 60 km/h in less than 7 s. Due to a special light-off catalyst, stoichiometric air-fuel mixture over the entire load and speed range and low row emissions, very low emissions are expected. The CO2 emissions are less than 60 g/km. A maximum speed of 100 km/h can be 38 reached, which guarantees the permission to be used on motorways. The driving range is approximately 160 km with the two full gas cylinders [CLEVER 2003]”. CLEVER Refuelling System “CLEVER is equipped with two removable gas cylinders with a capacity of 2 x 6 l CNG (Figure 3-3). To facilitate the use of CLEVER in regions with poor CNG infrastructure they can be externally refilled after removal from CLEVER. It is possible to exchange the cylinders e.g. at normal gas stations. However, the central conventional refuelling of both cylinders at natural gas filling stations without removal is possible, too [CLEVER 2003]”. Figure 3-3: CLEVER CNG engine and refuelling system at the back of the vehicle (© Naumann Design) CLEVER Tilting Mechanism “Due to the narrow track of the CLEVER vehicle, a tilting chassis is necessary to maintain stability in curves (Figure 3-4). An efficient hydraulic system is implemented to tilt the vehicle towards the centre of the curve. This is automatically controlled by an active direct tilt control system based on the driver’s input. This system also allows for car-like controls and has the advantage of keeping the vehicle upright while stationary [CLEVER 2003]”. 39 Figure 3-4: CLEVER tilting mechanism (© Naumann Design) CLEVER Safety “The main aim concerning safety was at least a 3 star rating in a EuroNCAP equivalent test procedure. The designated energy absorbing structure keeps the maximum cabin acceleration below 55 g. CLEVER has a two-chamber driver air bag and a belt system with pretensioner and a dual stage load limiter. Due to the stiff side structure of the cabin and the low vehicle weight, the intrusion can be limited with an expected intrusion velocity to be less than 8 m/s at a maximum crush of 125 mm [CLEVER 2003]”. CLEVER Use and Costs CLEVER is designed to be primarily used in urban areas for relatively short trips with the option to be used on motorways as well. Due to its small size advantages in parking can be gained. Its low emissions are a reasonable argument for promoting CLEVER and for supporting measures favouring the new vehicle in urban areas. The purchase costs are estimated about € 9.000,–. The running costs are due to the low fuel consumption and CNG costs estimated to be half of a conventional middle class car. The driver of a CLEVER has to hold a driving licence B. The overall aim of launching CLEVER is the substitution of car trips by CLEVER trips to achieve a reduction of CO2 emissions and emissions of hazardous air pollutants satisfying at the same time individual mobility needs [CLEVER 2003]. 40 4 Methodology 4.1 Research Flow The evaluation of the market potential of the new vehicle, its benefits for urban traffic and for the environment and the development of a CLEVER mode choice model is carried out in a stepwise approach (Figure 4-1). RP survey Scenarios for SP (postal paper and pencil household survey) SP survey (in-depth interactive interviews) Mode shift to CLEVER according to scenarios Discrete Choice Model Cost Benefit Analysis Survey Case study city Graz Data Analysis CLEVER vehicle concept Framework The frame is set by the CLEVER vehicle concept and one European case study city, where the hypothetical use of the new environmentally friendly vehicle is surveyed and tested under scenario conditions. A two stage mobility survey made up of a revealed preference survey (RP) and a stated preference survey (SP) is carried out in the selected case study city. Individual, trip and CLEVER related information are collected as well as mode shift data, which present the core of the SP survey. Data are coded, checked for their plausibility and weighted before they are analysed in view of a mode shift towards CLEVER according to the scenarios and the reasons for the choice/non-choice of CLEVER. Parameters of mode (CLEVER ) choice Results CLEVER market potential Effects and Impacts of CLEVER use Figure 4-1: Research flow diagram Based on the mode shift the potential use of CLEVER is estimated as well as the effects and impacts on urban traffic and the environment, which is done by means of a cost benefit analysis (CBA). Finally a discrete choice model explains influences on the mode choice and 41 in particular on the choice of CLEVER eliciting influencing parameters based on predefined hypotheses. 4.2 Selection of the Case Study City The selected city is medium sized (around 300.000 inhabitants) and suffers from more or less traffic and environmental problems respectively, which are assumed to be relieved by the use of CLEVER as a hypothesis. A relevant share of car driver trips in the cities’ modal split is favourable as particularly the mode shift from car traffic towards CLEVER is aimed at. A more practical argument is that socio-economic as well as transport and environmental related data have to be available. The use of CNG (compressed natural gas) in transport is not a precondition for the selection of a city. Finally the city of Graz in Austria has been chosen as a representative city of the middle of Europe. 4.3 Definition of Scenarios The use of CLEVER is amongst others dependent on measures favouring and promoting the new vehicle in urban areas. “Legal and regulatory measures can change the demand pattern in favour of sustainable modes like public transport, cycling and walking, and as such can reduce urban traffic problems and their negative impacts [LEDA 1999].” The demand for CLEVER is aimed at mitigating negative impacts on the environment especially reducing CO2 emissions, emissions of hazardous air pollutants and fuel consumption. The strategy to achieve this goal is to address potential CLEVER users by means of incentives. Measures have to create advantages for individuals whether financial or time benefits to cause a shift from the originally chosen mode towards CLEVER. However, one should be aware that only a substitution of trips made by individual motorised modes (car driver, moped/motorcycle) by CLEVER may cause the wanted effects. A shift from environmentally friendly modes (car passenger, public transport, bicycle, walking) to CLEVER results in an increase of kilometres travelled on the roads and consequently an increase of emissions, noise, accidents etc. has to be expected. “Measures are most effective when they are embedded in a total transport planning policy [LEDA 1999],” and when there are not only single but a bundle of measures aiming at the same goal. Three scenarios comprising measures to promote the use of CLEVER were compiled and provided the basis for the SP survey. 4.4 Survey Method A two-stage mobility survey was conducted in the case study city Graz to gain information and data on persons’ mobility behaviour as a basis for the CLEVER mode choice model. In a first step, the actual (revealed) behaviour was looked at in a RP survey by means of a trip diary of a normal working day – in this quantitative research questions like “who”, “what”, “where”, “when” and “how” were answered [BRADLEY 2004]. “Travel surveys generally collect information on the outcomes of decision processes. Trip/activity diary surveys, for example, tell us about the locations people decide to visit, the things they decide to do there, 42 and the modes they decide to use to get there. The surveys tell us little or nothing about how the people came to those decisions [BRADLEY 2004].” In a second step, a SP survey was conducted based on the outcome of the RP survey and on the three pre-defined scenarios. Additionally to the actual SP part asking about the hypothetical mode choice including the possibility to choose CLEVER under scenario conditions, qualitative “why” questions [BRADLEY 2004] were asked (e.g. motives and reasons for as well as barriers of the mode choice, availability of modes) to gain more elaborate information about how the person came to his/her decision. “The current relationship between SP and RP data collection provides a useful illustration: sometimes RP data is collected as an initial basis for customization in an SP survey and sometimes SP data is collected as a follow-up survey for a subsample of an RP survey, but the two types of data are often collected and analysed in a coordinated manner [BRADLEY 2004, p. 13].” 4.4.1 Revealed Preference Survey “The objective of RP surveys consists in rendering, through the survey, a valid and representative model of the real traffic behaviour of the target population [SAMMER G. (2003)].” In this case, real mobility behaviour is surveyed on a reporting day for each trip (trip diary) considering – mode of transport (on foot, bicycle, public transport, car driver, car passenger, moped/motorcycle), – trip purpose (commuting, business, education, shopping, leisure etc.) – trip destination (address), – trip length reported (km), – start and arrival time (resulting in travel time) and – number of passengers (in case of travelling by car). Additionally, mobility behaviour influencing key factors of potential CLEVER users are collected like – age, – gender, – education, – occupation, – driving licence, – ownership of vehicles, – ownership of a public transport pass. In a postal household survey, questionnaires – household/person and individual trip questionnaires according to the KONTIV® design [SOCIALDATA München (1975)] (Figure 4-2 and Figure 4-3) – have been sent to randomly selected households. A reminder procedure (postal and by phone) guaranteed a satisfactory response rate. 43 Figure 4-2: Household/person questionnaire of the RP survey (KONTIV® design) [SOCIALDATA München (1975)] 44 INDIVIDUAL QUESTIONNAIRE Please fill in number of person as assigned to him/her in the household questionnaire! Please complete for: Did you leave the house on the test day? Yes (Please give reason!) No, because Starting point of Other, please specify: your first trip: Address of your starting point: Home (Street, number) FIRST TRIP When did you start the trip? (Municipality) SECOND TRIP From here at: START: From here at: (Time) (Time) Why did you make the trip? What modes of transport did you use for this trip? Please tick all modes used! When you drove, how many passengers did you have? THIRD TRIP (Time) PURPOSE PURPOSE PURPOSE Work place Business trip Education/school Private visiting Shopping Active sports Trip home Others, please specify: Work place Business trip Education/school Private visiting Shopping Active sports Trip home Others, please specify: Work place Business trip Education/school Private visiting Shopping Active sports Trip home Others, please specify: Modes of transport Modes of transport Modes of transport On foot Bicycle On foot Bicycle On foot Bicycle Tram Public bus School/company bus Train Tram Public bus School/company bus Train Tram Public bus School/company bus Train Moped, Motorcycle Car driver Car passenger Taxi passenger Other, please specify: Moped, Motorcycle Car driver Car passenger Taxi passenger Other, please specify: Moped, Motorcycle Car driver Car passenger Taxi passenger Other, please specify: Number of passengers Number of passengers Number of passengers None None None Members of family Members of family Members of family (Number excluding driver) Other passengers (Number excluding driver) Other passengers (Number excluding driver) (Number excluding driver) Other passengers (Number excluding driver) (Number excluding driver) What was the destination of your trip? TRIP DESTINATION TRIP DESTINATION TRIP DESTINATION Please fill in the address in detail! (Street name) (Street name) (Street name) (Number) (Postal code) When did you arrive? (Municipality) ARRIVAL (Number) (Postal code) (Municipality) ARRIVAL (Time) Please estimate the length of the trip as exactly as possible! TRIP LENGTH about (Postal code) (Municipality) ARRIVAL (Time) TRIP LENGTH km (Number) about (Time) TRIP LENGTH km about km Please enter further trips and return trips in the next column! Figure 4-3: Individual questionnaire (trip diary) of the RP survey (KONTIV® design) [SOCIALDATA München (1975)] 45 Please turn over an d enter further trips of this day on the back side! First name: 4.4.2 Stated Preference Survey “In transport, the term “Stated Preference” has been the most-used term in the past to refer to surveys of future or hypothetical situations [LEE-GOSSELIN M. (2003)].” This term has turned slowly to the more general Stated Response (SR), which distinguishes four classes according to LEE-GOSSELIN M. (1996) (Table 4-1): Table 4-1: A Taxonomy of Stated Response Data Collection Methods [LEE-GOSSELIN M. (1996)] Focus on ... Examples of questions Stated Preference SP in the classical sense of choice experiments for conjoint analysis under the utility-maximisation framework “Given the levels of attributes in these alternatives, which would you prefer: [A] ... ? [B]... ? etc.” Stated Tolerance Limits of acceptability and thresholds for change: transfer Price/Willingness to Pay (WTP); Willingness to Accept; Contingent Valuation. “Under what circumstances could you imagine yourself doing: [r1] ... ? [r2] ...? etc.” Stated Adaptation Reactive and trial behaviour; problem solving under scenario conditions. “What would you do differently if you were faced with the following specific constraints: [.... detailed scenario].” Stated Prospect Learning processes, information seeking, metadecisions are viewed. “Under what circumstances would you be likely to change your travel behaviour and how would you go about it [... broad context].” In the recent SP survey – being aware of the SR taxonomy presented above, the term SP is still kept in this research – the hypothetical mobility behaviour of the interviewees was asked on the basis of the three pre-defined scenarios. In view of the defined SR classes, the SP survey can be specified as a crossover between Stated Preference and Stated Adaptation. Based on the trips reported in the RP survey, mode choice in the three scenarios was resumed, whereas different changes emerged concerning travel time and costs depending on the scenario and on the mode (car driver, car passenger, public transport, moped/motorcycle, bicycle) from which the shift towards CLEVER occurred. The objective was to test the user potential of the new CLEVER vehicle. “Over the years, SP data collection techniques have been tailored to provide data that mimic RP data as closely as possible in assuming that RP data is the best benchmark of external validity [BRADLEY 2004].” This ‘situational’ survey approach of using RP data as a basis for an SP survey e.g. is reported in travel demand research by BRÖG (1980) and by GOULIAS et al. (1998). Known as Intelligent Travel and Activity Diaries the survey “begins with a description of actual behaviour, similar to traditional household travel surveys, but then uses the reported behaviour as a basis for asking more in-depth, interactive questions that elicit perceptions and other subjective factors [BRADLEY 2004].” The survey was done by means of face-to-face interviews in households. A more detailed description of the SP research is presented in chapter 7. 46 4.5 Data Checks and Quality Control General data and plausibility checks were done to eliminate coding errors and implausibility due to unreliable or implausible answers of the respondents. TALATI A. et al. (2002) have determined quality controls for SP data: “Quality of the data is checked based on three parameters. Firstly, a check is made if the individual has taken the survey seriously and has made a choice at all. Secondly, the data is checked for any lexicographic answers, to see if the respondent is trading between the factors included in the survey. Finally a consistency check is performed to see whether the respondent makes a realistic choice, i.e., if the respondent has chosen the alternative with highest utility.” In this case, special attention is paid to the consistency and plausibility of answers concerning the choice of CLEVER in the scenarios. As CLEVER choice is hypothetical it has to be checked in detail, how realistic it is that the person, pretending in the interview to use CLEVER for a trip, would use and buy it in reality. Due to this consideration CLEVER choice in the scenarios is stepwise revised regarding the following constraints: – The potential CLEVER user needs to own a driving licence for a passenger car (category B). – Negative assessments of CLEVER (negative comments on purchase costs, aesthetic design etc.) lead to the assumption that one would not use and buy a CLEVER in fact. – If the question “Could you imagine to use the CLEVER?” (posed before the new mode choice according to the scenarios) is answered with “no” or “possibly” (plus comments suggesting that CLEVER would not be used), the choice of CLEVER is not reliable. – The question about the kind of availability of CLEVER (purchase, rental, car sharing) should be clearly answered with “purchase”, all the other possibilities (considering also the comments) may suggest that the use of CLEVER is not a permanent one. – The status of CLEVER (CLEVER as a single, second, third etc. vehicle) is examined in connection with the question about the replacement of other vehicles and the total number of cars in a household. Inconsistencies may again suggest that the reported use of CLEVER is not binding. If only one of these constraints is applicable (each trip respectively CLEVER choice is regarded separately), the choice is reset to the originally chosen mode. “Ensuring Quality in Stated Response Surveys” SAMMER G. (2003) emphasized the importance of considering (extract): – Consistency of the choices, – Respondents’ concentration and of the effect of fatigue, – Effect of large and small variations of the different attributes of alternatives, – Lexicographical answers, – Respondents’ well distributed trade-off between the alternatives offered. Being engaged with quality standards for Stated Response surveys LEE-GOSSELIN M. (2003) proposed quality issues specially related to the frame of survey design (objectives, sampling, instrument design, respondent conditioning, survey staff competence and quality control). 47 4.6 Data Analysis The analysis of the valid data set is carried out in order to investigate the potential changes in mobility behaviour due to the launch of the new environmentally friendly vehicle. The input data emerge from the RP and SP survey and are at first specified as mobility behaviour and characteristics of the sample. User requirements for using CLEVER are as well described as the mode shift according to the three scenarios. Barriers and influences on mode choice and on the choice of CLEVER in particular give details for the estimation of the CLEVER market potential and of potential target groups. Based on a descriptive analysis the focus of research is on two pillars (Figure 4-4): The mode choice model is based on the SP survey and aims at specifying the parameters of the CLEVER choice. Influencing variables emerge from both parts of the mobility survey. The impacts of the use of CLEVER on urban traffic and the environment are based on the expected mode shift towards CLEVER and are estimated by means of a cost benefit analysis. Input for the Mode Choice Model (SP) Mobility behaviour – RP CLEVER → User requirements Scenarios A, B, C Mobility behaviour – SP Mode Choice Model (SP) Modal Shift Impacts of CLEVER Use (CBA) Barriers and influences Potential target groups CLEVER Market potential Figure 4-4: 4.6.1 Relation of survey data, data analysis and results Descriptive and analytic statistics The description of the sample focuses at first on household, person and trip characteristics based on data of both parts of the survey. The modal split is in the centre of interest and is the origin for the mode shift from all types of modes (car driver, car passenger, public transport, bicycle, moped/motorcycle) towards CLEVER. The following questions will be covered: – From which modes does the mode shift towards CLEVER appear? 48 – How many trips can be substituted by CLEVER and how do the kilometres travelled change with different modes? – Which kind of trips (related to trip purpose and trip length) are covered by CLEVER? – Who are the potential CLEVER users? Who is the target group? – What are the objective as well as subjective reasons for and the constraints and barriers against the use of CLEVER? – What kinds of barriers as well as chances appear especially considering the use of environmentally friendly modes (public transport, bicycle, car passenger)? The correlation between mode choice and a selection of supposed influencing factors (age, gender, trip length, trip purpose, trip chaining) is analysed descriptively by means of cross tabulations first. In a second step, a correlation matrix covering all surveyed variables under the dependent variable of the chosen mode gives a general review on potential correlations and supports a pre-selection of potential variables to be considered in the mode choice model. While this approach only allows the correlation between two variables, the choice model considers interchanges between various variables at the same time. The analysis is carried out using SPSS 15.0 and Microsoft Excel 2000. 4.6.2 Discrete Choice Analysis The general objectives of modelling mode choice are to find causalities in travel behaviour and to predict the demand for travel. Disaggregate models reflect individuals’ behaviour based on discrete choice analysis method [BEN AKIVA M., LERMAN S. (1985)]. In this case, travel behaviour of individuals is modelled in view of the launch of the new environmentally friendly vehicle. CLEVER presents a new alternative in the “set of mutually exclusive and collectively exhaustive alternatives [BEN AKIVA M., LERMAN S. (1985)]”, from which the individual makes his/her choice under the assumption of utility maximization. “Briefly, a decision maker is modelled as selecting the alternative with the highest utility among those available at the time a choice is made [BEN AKIVA M., LERMAN S. (1985)].” Data used for the discrete choice analysis are mainly derived from the RP and SP survey under the assumption of hypothetical mode choice in three scenarios. Individual’s behaviour is modelled on trip level that means that each trip made by an individual on the reported day is considered independently with its attributes and the attributes of the traveller. It has to be kept in mind that this is an abstract approach, as the trips of an individual per day are interlinked and mode choice is not remade for each trip as it is a question of trip chaining. Due to segmentation of the data several model variants are calculated under different viewpoints and hypotheses. While models are developed for each scenario separately, one model including the data of all three scenarios is estimated as well. Segmentation according to the different modes (car driver, car passenger, public transport and bicycle), user groups (according to age or gender) or trip attributes (e.g. trip purpose) is imaginable; but it has to be taken into account that the number of cases of the segmented data in the sample still guarantees a satisfactory modelling result. The selected variables are based on behavioural hypotheses related to the individual trip makers under the viewpoint of availability of data, reliability and predictability [ARASAN V. T. (2003)]. A pre-selection of the variables is made on the basis of cross tabulations, identifying possible relations between mode choice and selected variables, and a correlation matrix including all useable variables. For the calculation of the mode choice model a logit model 49 (binary and/or multinomial logit) – the most frequently used one modelling travel demand and mode choice – is processed. The discrete choice analysis is carried out using the statistical software package „LIMDEP Version 8.0 / NLOGIT Version 3.0“ developed by Econometric Software, Inc. NLOGIT is a major suite of programs for the estimation of discrete choice models. 4.6.3 Evaluation of Effects and Impacts Based on the data of the SP survey and concretely on the (revised) modal split and mode shift towards CLEVER in the scenarios, the market potential of CLEVER is estimated. The most important effects of the use of CLEVER on urban traffic and the environment according to the scenarios are quantified and estimated by means of a cost benefit analysis (CBA). Those effects are identified using indicators related to the actual state. Reference data of the actual state are either calculated on the basis of the projected data of the revealed preference survey – guaranteeing validity for the whole viewed city – or taken from literature (e.g. emission loads, number of road accidents). The quantities in the scenarios are calculated considering the percentage change of the modal split and the change in kilometres travelled by the defined modes. The following indicators are used in the CBA: – Hazardous air pollutants and CO2 emissions, – running costs (including fuel consumption), – noise, – road accidents, – journey time, – required parking infrastructure and – welfare losses. 50 5 Sampling 5.1 Gross and Net Sample 5.1.1 Revealed Preference Survey The sample of the RP (household) survey is drawn randomly out of an official inhabitants register for Graz. The required useable net sample size is pre-defined with – 1.200 persons (> 6 years) and – guarantees that the sub-sample of 150 (selected) persons for the SP survey drawn out of the RP sample is reached. Table 5-1 shows the net sample of the RP survey in Graz including inhabitants of Graz travelling within and leaving the city boarders. The RP survey was completed between November 2003 and January 2004 with an extension to April 2004. The household response rate was 61% (from 838 contacted households 511 households returned the RP questionnaires). Table 5-1: Net sample of the RP (household) survey in Graz, 2003/2004 Number of … Households 511 Persons (> 6 years) 1.262 Trips 3.962 5.1.2 Stated Preference Survey The usable net-sample for the in-depth interviews of the SP survey is drawn out of the sample of the RP (household) survey and includes 150 persons, aged 16 years or older. It is a non-random sample as the interviewed households have been selected. The following criteria for the selection of the persons and the households respectively were defined: – At least one person per selected household should have had at least one car trip (as car driver). Altogether at least 100 persons with car trips (as car driver) should be interviewed. – All other mobile persons, who were aged 16 years or older and who took part in the interview, were valid for the net-sample as well. All household members aged 16 years or older should preferably had trips on the reporting day. – All household members aged 16 years or older should preferably take part in the interview. – Households, which had no car trips at all on the reporting day, were excluded a priori. All modes (car driver, car passenger, public transport and bicycle) are represented in the net sample. The net sample size of the SP interviews according to the units households, persons and trips are shown in Table 5-2. 51 Table 5-2: Net sample, selection and response rate of the SP survey in Graz, 2004 Net sample and response rate Households Total numbers Net sample RP survey 511 % of RP-households Selection for the SP survey Rejection Net sample SP survey 153 80 73 30% 16% 14% 52% 48% 442 292 150 35% 23% 12% 66% 34% 1.387 834 553 35% 21% 14% 60% 40% % of selected households Persons (Mobile persons > 16 years) Trips Total numbers 1.262 % of RP-persons % of selected persons Total numbers 3.962 % of RP-trips % of selected trips 153 households (30%) were selected out of the RP sample, 52% contacted households refused to participate or had not been reached. Interviews were carried out in 48% of the preselected households in Graz, which corresponds to a total of 73 households. 150 persons were interviewed in the SP survey, reporting 553 trips. The in-depth interviews were conducted in the period from April 2004 to June 2004. 5.2 Weighting and Grossing Up The aim of the weighting procedure is to avoid any bias of the target characteristics of the survey. Whenever bias is found, known or presumed to exist in the raw data set of a survey, weighting is required – for instance if the response rate is less than 100%. Generally, assessment of bias is done by comparing certain key variables of the sample with recent data of the population and other possible benchmarking sources [NEUMANN A. 2003]. Weighting was done on person level using official data of the population register of Graz. In this step the data are weighted using the socio-demographic characteristics of – household size (5 classes), – the cross distribution of age and gender (10 classes) and – the employment status (4 classes). The weighting was done simultaneously. The aim of a simultaneous weighting procedure is to produce consistent weights in several mathematically coordinated iteration steps. These weights have to satisfy all known distributions of characteristic variables of the population (census data) [SAMMER G., FALLAST K. (1996)]. Weights are iteratively adapted from the initial values to those values that fulfil the conditions represented by the distribution of the weighting characteristics in the population as a whole. After each weighting step on person level the total number of units (persons) was standardised to the original sample size. In Figure 5-1 the distribution of the weights are shown for Graz. The minimum value is 0,17, the maximum value is 4,45. 52 In order to get indicators of travel behaviour for the total population the results of weighting and grossing up procedures are multiplied according to the given numbers of the population in Graz. 25% 20% Share [%] 15% 10% > 2,85 2,35 - 2,85 1,95 - 2,35 1,61 - 1,95 1,33 - 1,61 1,10 - 1,33 0,91 - 1,10 0,62 - 0,75 0,51 - 0,62 0,42 - 0,51 0,35 - 0,42 < 0,35 0% 0,75 - 0,91 5% Classes of weights Figure 5-1: 5.3 Share of classes of person weights, Graz, n=971 persons Random Error of the sample The random error of the results of the SP survey as percentage values of the share of an attribute are presented in Figure 5-2 for Graz on person and trip level based on the values shown in Table 5-3 for a confidence level of 95%. The values are calculated according to P = p ± Δp with ⎛1 − p ⎞ ⎛ N − n ⎞ Δp = t ⋅ p ⋅ ⎜ ⎟⋅⎜ ⎟ ⎝ n ⎠ ⎝ N ⎠ 53 distinguished to person and trip level with: estimated value of the share of an attribute [%] absolute random error of the value of the share of an attribute for the confidence level of 95% [%] p share of an attribute [%] n sample size on person level: npers=150 persons sample size on trip level: ntrip=553 trips [persons]; [trips} N population on person level: Npers= 226.241 persons population on trip level: Ntrips= 692.683 trips [persons]; [trips] t t-value at normal distribution with 1,96 for a statistical reliability of 95% [ ] P Δp Table 5-3: Random error related to percentage values of the share of an attribute for Graz for a confidence level of 95%, person and trip level Person Level Graz Share of an attribute (mean value) Trip Level n N n N 150 226.241 553 692.683 Random Error (absolute) 5% ± 3,5% ± 1,8% 10% ± 4,8% ± 2,5% 15% ± 5,7% ± 3,0% 20% ± 6,4% ± 3,3% 25% ± 6,9% ± 3,6% 30% ± 7,3% ± 3,8% 35% ± 7,6% ± 4,0% 40% ± 7,8% ± 4,1% 45% ± 8,0% ± 4,1% 50% ± 8,0% ± 4,2% 55% ± 8,0% ± 4,1% 60% ± 7,8% ± 4,1% 65% ± 7,6% ± 4,0% 70% ± 7,3% ± 3,8% 75% ± 6,9% ± 3,6% 80% ± 6,4% ± 3,3% 85% ± 5,7% ± 3,0% 90% ± 4,8% ± 2,5% 95% ± 3,5% ± 1,8% 54 10,0% Trip level Person level Random Error [%] 8,0% 6,0% 4,0% 2,0% 95% 85% 75% 65% 55% 45% 35% 25% 15% 5% 0,0% Percentage value of the share of an attribute, person and trip level Figure 5-2: Random error related to percentage values of the share of an attribute for Graz for a confidence level of 95%, person and trip level 55 6 Case study city Graz 6.1 General Characteristics The city of Graz is the second largest town in Austria and the captial of the Austrian province Styria with a population of about 226.000 permanent residents [STADT GRAZ 2005]. Another 110.000 inhabitants, who daily commute to a large extent to Graz, live in the surrounding region. In the year 2003 more than 180.000 persons worked in Graz in about 10.600 jobs mainly in the tertiary sector (73,4% were engaged in public service, commerce or in the finance and insurance industry). Graz is established as a very important business and industrial location [GRAZ 2005]. The city has three universities with about 40.000 students, whereof the majority are not permanent residents. In the year 2003 Graz was elected as European Capital of Culture. The historic center of Graz as well as Schloss Eggenberg are part of the World Heritage Site (Figure 6-1). Graz is situated at the River Mur and is organised in 17 districts. Figure 6-1: 6.2 Historic center of Graz and Schloss Eggenberg Transport Policy, Infrastructure and Organisation Since the 1980ies transport policy has had high priority in Graz with the focus on “gentle mobility and soft policies” [STADT GRAZ 2005]. The promotion of walking, cycling and public transport on the one hand and the restriction of motorised private transport on the other hand have been implemented by a multitude of measures and activities (Figure 6-2). Nevertheless, car traffic is still in a leading position when looking at the modal split in Graz (in the year 2004: car drivers 38,2%, car passegers 9,1%; public transport 19,3%, cycling 14,1% and walking 19,3% [STADT GRAZ 2005]). Figure 6-2: Tramway and cycle path in Graz 56 The supply of transport infrastructure for the base year 2004 is characterised by the environmentally friendly transport policy of the past [STADT GRAZ 2005]: – Walking is promoted by an attractive, secure and “beeline” path network. Pedestrian zones are arranged in the inner city with a network length of 4,5 km. – Graz has a dense cycle path network, which currently comprises approximately 106 kilometres. Additional 84 km are planned to establish a complete network (190 km) throughout the city. – Public transport comprises six tram lines (with a total network length of 32 km) and 45 urban bus lines. – The road network covers about 1.000 km. A speed limit of 30 kilometres per hour on all streets except for priority streets aims at an enhancement of road safety and a reduction of emission loads in the city. – An inner city parking management scheme provides 24.000 parking spaces in chargeable short-term parking zones as well as in parking garages. Transport organisation in Graz is based on a zonal traffic concept characterised by a singlecentred town structure [SAMMER et al. 1994]. Five zones are arranged concentrically with differences in access for the transport modes: Zone 1 comprises the heart of the city centre – a pedestrian zone with assured crossing for public transport and bicycles. Motorised traffic is restricted to loading activities within time limits. Zone 2 surrounds the first zone and is accessible for public transport and bicycles. Car traffic is permitted permanently to residents only and to local business people and handicapped persons. A speed limit of 30 kilometres per hour is prescribed throughout the whole zone. In zone 3, access is assured for motorised traffic. All parking areas are chargeable and limited to 1,5 or 3 hours. Parking for longer periods is only possible in public garages. The 30 km/h speed limit is applied on all streets except priority streets. Bus lanes and tramway tracks guarantee that public transport can pass without constraints and receives priority at traffic lights. Zone 4 comprises the suburbs and residential neighbourhood. On-street parking is for free as parking management is not applied. Zone 5 is defined as the surrounding countryside. Attractive public transport lines as well as park-and-ride and bike-and-ride facilities are provided. This zonal concept is the basis for the generation of scenarios promoting CLEVER in the SP survey. 57 7 SP Survey While in the RP survey reported actual behaviour is collected, the SP survey aims at eliciting the most probable travel behaviour for hypothetical scenarios. In this case the new environmental friendly CLEVER and its use and market potential of purchasing the new vehicle respectively are in the focus of interest. In addition to the defined scenarios and the choice situation, some more information and data are collected in an in-depth interview. 7.1 7.1.1 The in-depth interview Interview process The interview is conducted face-to-face in several steps (Figure 7-1). The sequence of questions enables the respondents first to recall their revealed mobility (including availability of modes, knowledge and willingness of and reasons for mode choice) in preparation for the hypothetical choice process based on the launch of the new alternative CLEVER. Availability of vehicles/modes of transport Activities on the reporting day (Individual questionnaires of the RP survey) Reasons for the mode choice Experiences with other modes of transport Presentation of the CLEVER-vehicle Assessment of the CLEVER-vehicle Presentation of Scenarios A, B, C Reactions on Scenarios (Mode choice) Stated choice CLEVER related and general questions Figure 7-1: Stages of in-depth interview In this regard, the interview starts with an “ice-breaking game” defining the availability of vehicles in the household and of modes of transport in a discussion among the participating household members – preferably all members aged 16 or older. Then each participant fills in the trip questionnaires (compare the following chapter 7.1.2 and Annex) resuming the reported trips of the RP survey and answering more detailed questions about reasons for mode choice, experiences with other modes etc. That follows a presentation of CLEVER 58 (pictures and characteristics) by the interviewer. The respondents get the task to assess the vehicle and its use. The different scenarios (A, B, C) are presented by the interviewer subsequently. According to them the interviewees have to reconsider their travel behaviour and mode choice – this stated choice is in fact the actual core of the SP suvey. The interview ends with some general questions concerning CLEVER (purchase availability, replacement of vehicles in the household etc.). 7.1.2 Questionnaires and survey materials According to the stages of the in-depth interview a number of forms and survey materials are developed with respect to the objectives and issues, which are addressed. Figure 7-2 gives an overview of all materials used for one interview. The original forms and questionnaires are presented in the Annex. The interviewer entry pattern (IEP) 0a and 0b are to enregister the participating household members and for organisational issues filled in by the interviewer. Availability of vehicles and modes for the participants is entered into the forms IEP 1 and A1, which is designed as a board with additional cards. Each participant has to state the frequency of use of each shown mode putting the appropriate card (“+ frequent use”, “- never used“ or “0 occasionally used”) on the board. This unconventional start of the interview should contribute to a more familiar atmosphere (“ice-breaker game”). IEP 2a summarises the trip diary of each participant gained at the RP survey. It serves as a reminder for more detailed “why questions” – about motives for the use of the chosen modes and barriers against the use of alternatives. Each single trip is recalled in Q1a – Q1d, whereas the minuscule identifies the chosen mode (a = car driver, b = car passenger, c = PT, d = bicycle). In IEP 2b PT alternatives are presented for car driver, car passenger and bicycle trips – for each trip of each participant. This information is pre-detected by the interviewers by means of online schedules for a comparison with the actual knowledge of motorists about PT supply. After discussing the revealed trips, the new alternative CLEVER is presented by means of pictures and a description (IS 1, IS 2a – 2c). In Q 2 CLEVER has to be assessed by the particpants (compare Annex). Several features of CLEVER (idea, design, size, speed, environmental friendliness etc.) are rated on a 4-stage scale and a first hypothetical use is asked for. The actual part of the SP survey includes IEP 3, which is a brief reminder of the trip diaries of all household members, the presentation and description of the three scenarios (IS 3a, IS 3b, IS 3c) and finally the stated choice questionnaires (Q 3a – Q 3d, again differentiated according to modes of transport). The stated choice experiment is tailored for each participant and refers to the trips stated in the RP survey. For those who have stated to use CLEVER in one of the three scenarios Q4 is prepared, including questions about the availability of the new alternative e.g. purchase, rental, sharing. Finally the household income is asked for in Q5. Despite the large number of questionnaires the whole interview should not exceed the length of one to one and a half hours (depending on the number of particiting family members). 59 IEP 0a Participants of the interview, Interviewer explanation IEP 0b A1 “Ice breaker game”: Availability of modes/vehicles IEP 1 IEP 2a IEP 2b Q 1a Trip diary (RP), PT supply Q 1b IS 1 Q 1c IS 2a IS 2b Q 1d Trip questionnaires Q2 IS 2c CLEVER introduction, assessment IEP 3 Reminder: trip diaries IS 3a IS 3b IS 3c Q 3a Q 3b Q 3c Q4 Q5 Description of scenarios Q 3d Stated choice questionnaires Use of CLEVER, Income Abbreviations: Q Questionnaire for interviewees (to be filled in by the interviewees) IS Information sheet (Information for the interviewees) IEP Interviewer entry pattern (to be filled in by the interviewer) PT Public transport IIN Individual identification number MT Means of transport A3 Figure 7-2: 7.1.3 A4 ... Formats of the forms and questionaires Forms, questionnaires and information sheets for each interview step Role of the interviewer As the survey was done by means of face-to-face interviews at the interviewees’ home, the role of the interviewer was a very sensitive one. This fact and the variety and plenty of information to be gathered, required some preparation and training for the interviewers. Each interviewer was guided how to proceed and got a detailed interview instruction. Each interviewer had to be well trained and ready for any kind of questions of the interviewees. 60 The interviewer contacted the selected households/interviewees by phone asking for their willingness to attend the interview. If they agreed, an appointment was made. Before the meeting, the interviewer had to prepare the questionnaires for the interviews. Existing trip information from the RP survey was filled into each trip questionnaire as a reminder. Trip characteristics (travel time and costs with the originally chosen mode and with CLEVER) were pre-coded in the stated choice questionnaires due to a defined estimation (compare chapter 7.2.4). During the interview the interviewer should take a neutral position in respect of CLEVER. His/her task is to ensure a smooth run of the interview and to give support to the interviewees in case of ambiguities. The interviewer should naturally have no influence on the stated mode/CLEVER choice of the interviewees. However, there is still a risk that the interviewer impairs the reliability of the results of the respondents’ mode choice in the SP part of the survey. This potential influence is examined in the mode choice model (compare chapter 11.4.7). 7.2 7.2.1 Design of the Stated Choice Experiment Tailoring Stated Choice “… Thus, depending on the specific circumstance of the study, what may be required is the tailoring of SC (stated choice) experiments and experimental designs to reflect the choice context that is likely to be faced by each individual sampled respondent. This, however, challenges current design practice in which it is common to construct a single experimental design and apply this single design to all sampled respondents, irrespective of the true choice situation faced by each individual respondent [ROSE, HENSHER (2004, p.3)].” The SP experiment has been designed according to this approach of tailoring stated choice, which is also recommended by SAMMER G. (2003) “To make it possible for the respondent to make his/her decision in a familiar situation, SR choices should be based on the actually revealed travel behaviour (basis alternative), e.g. reported trips of a concrete day.” Based on the trip diaries of the RP survey each respondent has to reconsider his/her own mode choice for each single trip under scenario conditions. Depending on the number of trips a respondent made on the reporting day, he/she has to take that number of new decisions one after the other multiplied by three (scenarios). Although each trip based decision is actually taken for its own, in most of the cases it is not an independent decision as trip chaining has to be considered. The choice set for each trip comprises two alternatives – the originally chosen mode and the new alternative CLEVER (exemption: Scenario C for car drivers; compare chapter 7.2.3 and Figure 7-4 ). The trips are specified by the attributes “travel time” and “travel costs”, which are calculated in advance according to defined assumptions (compare chapter 7.2.4). The common experimental designs as for example full or fractional factorial or orthogonal designs [ROSE J., BLIEMER M. 2007] have not been implemented (compare chapter 11.1.3), as the intention was to stick closely to the individuals’ trips under the assumption of realisitc and consistent changes of the attributes of alternatives due to the definition of the scenarios. Two examples of applied stated choice questionnaires are shown in Figure 7-3 and Figure 7-4. A brief summing-up of the trip and the trip attributes “travel costs” and “travel time” for the particular mode are given in the heading. In the ideal case the respondent makes his/her 61 mode choice due to those specifications. Additional questions about changes in mobility behaviour choosing the alternative CLEVER and stated reasons for mode choice aim at drawing a more detailed picture of the decision making process. CAR DRIVER Interview No. IIN Q 3a Scenario A First name Trip No. Start time SCENARIO Trip length Purpose of the trip A Trip costs: Duration of the trip: Which mode would you use under these (altered) conditions for this trip? therefrom fuel costs CAR € € CLEVER € € CAR Min. CLEVER Min. CAR CLEVER Would you change your destination? If yes, to what extent (purpose, destination)? Yes No Would you do any other trips additionally? If yes, which? Yes No Would you start your trip earlier/later? If yes, why? Yes No Start time: Reason: Does the change of your mode choice affect the trips of the other household members? Yes No If yes, to what extent? Please give reasons for your mode choice: Figure 7-3: Stated choice questionnaire for a car driver trip (Q 3a) for scenario A 62 CAR DRIVER Q 3a Scenario C Interview group Start time SCENARIO Trip length Purpose of the trip therefrom fuel costs C Trip costs: Duration of the trip: Would you skip this trip? Which mode would you use under these (altered) conditions for this trip? CAR € € CLEVER € € PT € Bicycle € CAR Min. CLEVER Min. PT Min. Bicycle Min. On Foot Min. No Yes Car passenger CAR CLEVER PT Bicycle On Foot Would you change your destination? If yes, to what extent (purpose, destination)? Yes No Would you do any other trips additionally? If yes, which? Yes No Would you start your trip earlier/later? If yes, why? Yes No Start time: Reason: Does the change of your mode choice affect the trips of the other household members? Yes No If yes, to what extent? Please give reasons for your mode choice: Figure 7-4: 7.2.2 Stated choice questionnaire for a car driver trip (Q 3a) for scenario C Scenarios of the SP Survey For the SP survey three scenarios, comprising measures to promote the use of CLEVER, are compiled and presented to the respondents. Each scenario is prepared to result in a new 63 decision process for the interviewees. The variables “travel costs” and “travel time” vary across the scenarios and should be the crucial criteria for the new/hypothetical mode choice. 7.2.2.1 Scenario A In Scenario A the CLEVER vehicle is launched at the market. An area wide supply of gas stations offering CNG is a precondition for the use of CLEVER. Sales, distribution and service supply is guaranteed. Beside the option to buy CLEVER, CLEVER sharing and rental is offered. There are neither infrastructure nor any additional organisational measures related to the present situation planned. LAUNCH of CLEVER – a new motorised private vehicle Area wide supply of gas stations for vehicles like CLEVER run by compressed natural gas (CNG) Sales and distribution of CLEVER in the whole country Good service supply (garages etc.) for CLEVER Supply of “CLEVER rental” and “CLEVER sharing Advantages and disadvantages of the use of CLEVER in comparison to the originally chosen modes in Scenario A regarding the trip characteristics “travel time” and “travel costs” are depicted in Table 7-1 for all reported trips: While there are no time advantages for CLEVER compared to car driver, car passenger and moped/motorcycle rider, one may be faster using CLEVER instead of public transport or bicycle. Cost advantages for CLEVER appear, compared to car driver, due to the lower fuel prices (CNG) for CLEVER, and to some extent compared to public transport. Table 7-1: Advantages/disadvantages of the use of CLEVER regarding travel time and travel costs in Scenario A in comparison to originally chosen modes for the reported trips SCENARIO A Mode shift from … to CLEVER as driver Travel time Travel costs Car driver o + Car passenger o – Motorcyclist o o Public Transport user + +/– Cyclist + – + … advantage for CLEVER o … no difference – … disadvantage for CLEVER 64 7.2.2.2 Scenario B This scenario includes “pull-measures” (= measures favouring the new vehicle) that could be implemented with low financial effort as nearly no infrastructure has to be built, only slight adaptations on the road space are foreseen. While the use of bus lanes and designated parking spaces for CLEVER in the city centre implicate time advantages for the CLEVER user, an exemption from road pricing and reduced parking fees guarantee additional financial benefits (Table 7-2). Policies to promote CLEVER in the city/area No parking fees and no time limitations in on-street parking zones for CLEVER; Reduced parking fees (– 50%) in garages for CLEVER Priority parking sites for CLEVER in the city centre and at P&R locations (in garages and on-street) Use of bus lanes with CLEVER in the city Exemption from road pricing Table 7-2: Advantages/disadvantages of the use of CLEVER regarding travel time and travel costs in Scenario B in comparison to originally chosen modes for all reported trips SCENARIO B Mode shift from … to CLEVER as driver Travel time Travel costs Car driver + + Car passenger + – Motorcyclist o o Public Transport user + +/– Cyclist + – + … advantage for CLEVER o … no difference – … disadvantage for CLEVER 65 7.2.2.3 Scenario C Scenario C includes all measures of Scenario B and the launching activities of Scenario A. In addition to Scenario B “push-measures” (= restrictions and constraints for car drivers aiming at triggering a mode shift towards environmentally friendly modes) are considered, which are limited to the raise of fuel prices by 75% or 150% (splitting of the sample with the objective to find out elasticities of the reaction to that measure). As a consequence the difference in travel costs between car and CLEVER increases (Table 7-3). Policies to promote CLEVER in the city/area according to Scenario B and at the same time increase of the fuel prices of 75 % or 150 % throughout the country. * Petrol 1,487 €/l or 2,125 €/l * Diesel 1,295 €/l or 1,85 €/l * Status 2003: Petrol 0,85 €/l Diesel 0,74 €/l Table 7-3: Advantages/disadvantages of the use of CLEVER regarding travel time and travel costs in Scenario C in comparison to originally chosen modes for all reported trips SCENARIO C Mode shift from … to CLEVER as driver Travel time Travel costs Car driver + + Car passenger + – Motorcyclist o o Public Transport user + +/– Cyclist + – + … advantage for CLEVER 7.2.3 o … no difference – … disadvantage for CLEVER Alternative modes in the scenarios „The presence or absence of an alternative in the choice sets offered to an individual should reflect the specific context or situation faced, or likely to be faced, by that individual, in order to maximise the realism of the experiment for that individual [ROSE, HENSHER (2004, p.3)].” The alternatives for mode choice in the three scenarios are dependent on the originally chosen mode and on the scenarios themselves. In Scenario A and Scenario B two alternatives are available – the originally chosen mode and CLEVER (Table 7-4). As the mode choice is supposed to be made due to the advantages/disadvantages the respective scenario provides, the choice of any other alternative would not be rationally explainable – as the scenario conditions do not imply any impairments of the use of the originally chosen modes. In Scenario C there are some more alternatives for car driver, car passenger and motorcyclist beside CLEVER, as in Scenario C the cost argument appears due to rising fuel 66 prices. This means that these transport users have a significant disadvantage using their actually chosen mode in Scenario C compared to the actual state (compare previous chapter). For public transport users and cyclists in Scenario C CLEVER is still the only alternative as any other alternative does not provide an evident advantage and therefore would not be rationally explainable. Table 7-4: Possible alternatives for mode choice in the three scenarios according to the originally chosen mode Possible alternative modes in the three scenarios Originally chosen mode Scenario A Scenario B Scenario C Car driver CLEVER Car driver Car driver Car driver CLEVER CLEVER Car passenger Public Transport Bicycle On foot Trip skipped Car passenger CLEVER Car passenger Car passenger Car passenger CLEVER CLEVER Car driver Public Transport Bicycle On foot Trip skipped Moped/motorcycle CLEVER Moped/motorcycle Moped/motorcycle Moped/motorcycle CLEVER CLEVER Car passenger Public Transport Bicycle On foot Trip skipped Public Transport Bicycle 7.2.4 Public Transport Public Transport Public Transport CLEVER CLEVER CLEVER Bicycle Bicycle Bicycle CLEVER CLEVER CLEVER Assumptions and calculation patterns 7.2.4.1 Interview groups The calculation and generation of the values of attributes of alternatives for the tailored SP part of the survey are generally based on the assumption of an increase of travel costs for car driver trips and time savings using CLEVER in the three scenarios. To respect orthogonality at least to some extent – tailoring the SP situation actually neglects this approach due to the demand of creating as realistic scenarios as possible to the respondents – different attribute levels are varied. In this regard all households are allocated to one of 4 67 possible interview groups (Table 7-5). The groups differ in terms of attributes of alternatives “travel time” and “travel costs” in scenario B and C. As a result varying reactions of the respondents to the projected measures are expected. Table 7-5: Interview groups in the SP sample Interview group 1 Interview group 2 IC / IT IC / IIT IIC / IIT IIC / IT Interview group 4 Interview group 3 Index C ... Costs; Index T … Time IC ... Fuel price increase in scenario C by 75% IIC ... Fuel price increase in scenario C by 150% IT ... 10% time savings with CLEVER in scenario B and C IIT ... 40% time savings with CLEVER in scenario B and C 7.2.4.2 Variable “travel costs” Travel costs were calculated for all trips of the interviewees reported in the RP survey. They were precoded in the SP experiment for the chosen mode as well as for the possible alternatives in the three scenarios (compare chapter 7.2.3). Calculations are based on defined pattern shown in the following tables. The column “mode of transport” in the tables needs some explanation: While “car” and “moped/motorcycle” represent the originally chosen modes, CLEVER is always the alternative for all modes in the scenarios. “Bicycle” and “PT” are originally chosen modes as well as alternatives for car trips in scenario C. “Interview groups” are relevant for car and CLEVER trips in scenario C. Travel costs (total as well as fuel costs) for car, moped/motorcycle and CLEVER depend above all on trip length [km], which has been estimated and reported in the RP survey by the resondents (Table 7-6). While this part of the total costs is variable, there is a fixed one per scenario, which is based on the official kilometer allowance* (Status 2003). For CLEVER it has been defined that this fixed value is equal to that for motorcycles. The differentiation between the scenarios reflects the defined assumptions (compare chapter 7.2.2). (* The official kilometer allowance includes loss in value, fuel and oil, maintenance and repairing, snow tyres, taxes and insurance, memebership costs of automobile clubs, financing costs.) 68 Table 7-6: Calculation of total travel costs for car, moped/motorcycle and CLEVER PATTERN 1 – “Total travel costs in A, B and C for car, moped/motorcyle, and CLEVER“ for Q 3a (Car driver and moped/motorcycle), Q 3b (Car passenger), Q 3c (PT), Q 3d (Bicycle) Calculation depends on the interview group: Mode of transport Scenario A IC CAR Scenario B IC km1) x 0,40 € IIC CLEVER Moped/ motorcycle 1) km: IC km1) x 0,40 € IIC IC km1) x 0,20 € IIC km1) x 0,18 € IIC Scenario C IC km1) x 0,46 € IIC km1) x 0,52 € IC km1) x 0,20 € IIC km1) x 0,22 € Trip length – Information from the personal questionnaire of the RP survey Calculating fuel costs for cars an average consumption of 8 l/100 km and a mean out of petrol and diesel prices are assumed and result in [km x 0,07 €] (Table 7-7). Due to the lower prices for CNG, fuel costs for CLEVER are less than half the costs for cars. In scenario C the defined rise of fuel costs – for petrol, diesel and CNG – has been considered. Table 7-7: Calculation of fuel costs for car, moped/motorcycle and CLEVER PATTERN 1a – “Fuel costs of the trip in A, B und C for cars, mopeds/motorcycles and CLEVER“ for Q 3a (Car driver and moped/motorcycle) Calculation depends on the interview group: Mode of transport Scenario A IC CAR Scenario B IC 1) km x 0,07 € 1) km: IC IC km1) x 0,12 € IIC km1) x 0,18 € IC km1) x 0,05 € IIC km1) x 0,08 € km x 0,07 € IIC CLEVER Moped/motorcycle 1) Scenario C IIC IC 1) km x 0,03 € 1) km x 0,03 € IIC IIC Trip length – Information from the personal questionnaire of the RP survey The calculation of costs for a bicycle trip include the trip length [km], which has been estimated and reported in the RP survey by the resondents, and the estimated costs per kilometer (0,05 €) (Table 7-8). A differentiation across the scenarios was not necessary as no (financial) measure supporting bicycle trips has been projected. Table 7-8: Calculation of travel costs for bicycle PATTERN 2 – “Travel costs in A, B and C for bicycle“ for Q 3a (Car driver and moped/motorcycle), Q 3b (Car passenger), Q 3d (Bicycle) Mode of transport Scenario A Scenario B km1) x 0,05 € BICYCLE 1) km: Trip length – Information from the personal questionnaire of the RP survey 69 Scenario C PT costs depend on whether or which PT pass the interviewee owns (compare Table 7-9). Beside public transport as an orignally chosen mode it is an alternative in scenario C for car trips. Table 7-9: Calculation of travel costs for Public Transport PATTERN 3 – “Travel costs in A, B and C for PT“ for Q 3a (Car driver and moped/motorcycle), Q 3b (Car passenger), Q 3c (PT) Mode of transport Scenario A Scenario B Scenario C Depending on whether or which PT pass the interviewee owns (see household questionnaire): PT - For pupils: 0 € - PT weekly, monthly or annual ticket in Graz: 0,75 € - Ordinary ticket: 1,60 € (considering the different PT-zones in the periphery of Graz) 7.2.4.3 Variable “travel time“ The variable “travel time” was calculated for the alternative mode CLEVER in the scenarios and for bicycle, walking and PT additionally in scenario C. The duration of the trips done with originally chosen modes is a kown value as it has been stated by the respondents in the RP survey. According to the definition of the scenarios, time savings can be gained by car drivers using CLEVER in scenario B and C (Table 7-10). Two levels are distinguished according to the interview groups – 10% and 40% time savings with CLEVER due to the use of bus lanes etc. For motorcyclists no time advantage can be gained using CLEVER. Table 7-10: Calculation of travel time for CLEVER as an alternative for car driver and car passenger and moped/motorcycle PATTERN 4 – “Travel time in A, B and C for CLEVER“ for Q 3a (Car driver), Q 3b (Car passenger) Attention! Different for moped/motorcycle-trips! Calculation depends on the interview group: Mode of transport Scenario A Scenario B CLEVER (for car driver and car passenger) IT Scenario C 2) 3) d [minutes] – 10% (rounded to the nearest whole minute) Travel time as given in the RP survey IIT 2) 3) d [minutes] – 40% (rounded to the nearest whole minute) CLEVER Trip duration as given in the RP survey (for moped/motorcycle) 2) 3) d = Trip duration as given in the RP survey 10% or 40% time saving due to the use of bus lanes, reduced time spent for looking for a parking space etc. 70 The calculation of travel time for CLEVER as an alternative for bicycle and PT is based on the trip length [km] reported by the respondents in the RP survey and on an average speed of CLEVER in uban areas, which has been assumed to be 30 km/h (Table 7-11). Time savings in scenario B and C are calculated according to the interview groups. Table 7-11: Calculation of travel time for CLEVER as an alternative for bicycle and PT PATTERN 4a – “Travel time in A, B and C for CLEVER“ for Q 3c (PT), Q 3d (Bicycle) Calculation depends on the interview group: Mode of transport Scenario A CLEVER Scenario B (km1) x 2) + 5 min. IZ Scenario C 1) 3) ((km x 2) + 5 min.) – 10% (rounded to the nearest whole minute) [Assumption: vCLEVER = 30km/h; 1km = 2 minutes] IIZ ((km1) x 2) + 5 min.) – 40%3) (rounded to the nearest whole minute) 1) km: 3) 10% or 40% time saving due to the use of bus lanes, reduced time spent for looking for a parking space etc. Trip length – Information from the personal questionnaire of the RP survey As in scenario C car drivers and passengers have more alternatives available than CLEVER, travel time – using a bicycle and for walking – had to be estimated (Table 7-12). It is again based on the trip length [km] reported by the respondents in the RP survey and on an average speed for those modes. A possible PT alternative has been identified in advance and could be found in the form IEP 2b. Table 7-12: Calculation of travel time for cycling and walking as alternatives for car (driver and passenger) in scenario C PATTERN 5 – “Travel time in A, B and C for cycling and walking“ for Q 3a (Car driver and. moped/motorcycle), Q 3b (Car passenger) Mode of transport Scenario A Scenario B – – Scenario C km1) x 5 min. BICYCLE [Assumption: bicycle speed = 15 km/h] km1) x 15 min. WALKING 1) km: – – [Assumption: walking speed (pedestrian) = 4 km/h] Trip length – Information from the personal questionnaire of the RP survey 71 8 Data, descriptive analysis This chapter provides a description of the data (in this case weighting is not considered) of the two parts of the survey – the RP and the SP survey – approaching the following questions: How is the sample characterised? What kind of information has been collected? Which basic variables are available for the mode choice model? Three levels of characteristics can be distinguished each identifying and specifying a certain group of variables: – Characteristics of households – Person characteristics – Trip characteristics. The most important variables, specified by some statistical indicators are presented subsequently describing the RP and the SP sample. The sample size of the RP and SP survey is shown in Table 8-1 according to the allocation above. The differences of the indicators of the variables between both surveys result from the systematic selection of car driver trips for the SP survey out of the RP sample. For a representative analysis of the data (especially regarding the modal split in the subsequent chapter 1) weighting as well as a correction of that disbalanced ratio of RP and SP data have to be considered. Table 8-1: Sample size of the RP and SP survey in Graz, 2003/2004 Number of … RP SP Households 511 73 (Mobile) Persons (> 16 years) 1.262 150 Trips 3.962 553 8.1 Characteristics of households The most common and interesting characteristics of a household include the variables “size”, which gives the number of persons per household, “children”, comprising the number of children younger than 6 years in a household, and “cars”, specifying the number of private cars per household. In Table 8-2 the statistical indicators (mean, standard error and range) of those variables are presented. The question about the “household income” has been asked for only in the SP survey with the result presented in Figure 8-1. 72 Table 8-2: Summary of some characteristics of households in the RP and SP sample, 2003/2004 Households RP (n = 511) Variable Mean SP (n = 73) Standard Error Range Mean Standard Error Range Size (Number of persons/household) 2,69 0,057 1–7 3,23 0,132 2–7 Children < 6 years (yes = 1) 0,17 0,017 no / yes 0,16 0,044 no / yes - - - 1,70 0,093 1–4 Cars (Number of cars/household) 50,0% SP n = 73 households 40,0% 39,7% 30,0% 30,1% 20,0% 16,4% 13,7% 10,0% 0,0% up to € 2.000 Figure 8-1: 8.2 € 2.000 - € 3.000 > € 3.000 not specified Household income in Graz as a result of the SP survey, 2003/2004 Person characteristics The variables collected in the RP survey to describe the persons include amongst others “gender”, “age”, “education”, “employment”, (ownership of a) “driving licence”, “cars” (number of cars per person) and number of motorcycles and bicycles per person. A comparison of the basic statistics of person characteristics of both samples (Table 8-3) shows that the average age of the respondents is a little bit higher in the SP sample than in the RP sample (Figure 8-2), the ratio of men and women is nearly the same, while the vehicle ownership (number of cars, motorcycle and bicycles) as well as “holding a driving licence” are slightly higher in the SP sample. The distribution of the values of the variable “education” characterise the SP sample to include mainly higher educated persons (Figure 8-3). Regarding the variable “employment” the share of full-time employed persons is higher in the SP sample as well (Figure 8-4). 73 Table 8-3: Summary of some characteristics of respondents in the RP and SP sample, 2003/2004 Persons RP (n = 1.262) Variable Mean Gender (Male = 1) SP (n = 150) Standard Error Range Mean Standard Error Range 0,47 0,014 female / male 0,50 0,041 female / male 41 0,571 6 – 95 47 1,325 17 – 84 Driving licence (yes = 1) 0,80 0,012 no / yes 0,93 0,022 no / yes Cars (number of cars per person) 0,48 0,016 0–3 0,63 0,047 0–3 Motorcycles 0,08 0,008 0–3 0,13 0,030 0–2 Bicycles 0,65 0,018 0–5 0,72 0,047 0–3 PT ticket (yes = 1) 0,33 0,014 no / yes 0,29 0,040 no / yes Age (years) 50,0% 40,0% 39,3% 30,0% 31,3% 32,0% 27,5% 20,0% 16,4% 10,0% 15,3% 13,3% 12,5% 10,8% 1,5% 0,0% < 16 years 16 - 25 years RP n = 1.262 persons Figure 8-2: 26 - 45 years 46 - 65 years > 65 years not specified SP n = 150 persons Age classes in Graz as a result of the RP and SP survey, 2003/2004 74 50,0% 40,0% 36,7% 33,9% 30,0% 29,3% 20,0% 23,3% 22,1% 18,3% 10,0% 14,3% 11,4% 5,3% 5,3% 0,0% Primary and secondary school Primary, secondary and vocational school A-levels (university entrance exam) RP n = 1.262 persons Figure 8-3: University degree not specified SP n = 150 persons Education in Graz as a result of the RP and SP survey, 2003/2004 60,0% 55,0% 50,0% 48,0% 40,0% 41,3% 30,0% 36,0% 20,0% 10,0% 9,0% 10,7% 0,0% full-time employed RP n = 1.262 persons Figure 8-4: 8.3 part-time employed not employed SP n = 150 persons Employment in Graz as a result of the RP and SP survey, 2003/2004 Trip characteristics The average number of trips per person and day is 3,64 in the RP and 3,95 in the SP sample including trips with all kinds of modes – also walking – and trips home, with a range from 0 to 13 trips per day. The number of egresses per person and day (this means how often a person leaves his/her home for a certain purpose) varies from 0 to 6 (RP) and from 1 to 4 (SP) and is on average 1,51 in both samples (Table 8-4). The most important trip characteristics include the variables “travel time (min)”, “trip length (km)”, of course the chosen mode and “trip purpose”. Travel time as well as length of a trip are estimated values specified by the respondents in the trip diary of the RP survey. The average travel time is in both samples about 23 minutes per trip, whereas the range is more 75 distinctive in the RP sample – there are trips twice as long as in the SP sample (Table 8-4). The average trip length varies from 7,6 km in the RP to 10 km in the SP sample. Table 8-4: Summary of some trip characteristics in the RP and SP sample, 2003/2004 Trips RP (n = 3.962 trips incl. walking trips) SP (n = 553 trips) Variable Mean Mean Standard Error Range Standard Error Range Trips (Number of trips/ person and day) 3,64 0,057 0 – 13 3,95 0,166 1 – 13 Egress (Number of egresses/person and day) 1,51 0,022 0–6 1,51 0,058 1–4 Travel time (min) 22,62 0,346 1 – 395 23,15 0,868 1 – 180 Trip length (km) 7,56 0,283 0,10 – 280 10,05 0,823 0,10 – 200 8.3.1 Modal Split The modal split is the most important trip characteristic and variable of the survey – after all it provides not only the basis for the evaluation of effects and impacts of traffic and mobility but also for further analyses and especially for the mode choice model, where the chosen mode presents the dependent variable among two or more alternatives. The following transport modes are considered: – Car driver – Car passenger – Public transport – Bicycle – Moped/motorcycle – Walking. The modal split resulting from the RP survey (Figure 8-5) fits quite well with the actual (grossed up) model split in Graz (Figure 8-6). As Graz is well known for its bicycle friendly infrastructure the high share of bicycle riders is not surprising. Generally it is worth mentioning that more than 60% of all trips – which is a relatively high percentage – is done with environmentally friendly modes (bicycle, public transport, car passenger and on foot). The systematic selection of the interviewees in the SP survey (compare chapter 5.1.2) explains the high share of car driver trips in this sample (Figure 8-5). Walking is not included, as it is not the target to substitute walking by CLEVER trips. 76 4,2% 9,4% 18,5% 1,3% 11,9% 41,7% 10,1% 64,2% 10,3% 18,8% 9,6% RP: n = 3.692 trips Car driver Figure 8-5: SP: n = 553 trips Car passenger Public Transport Bicycle Moped/ motorcycle On foot Modal split in Graz based on the RP and SP survey, 2003/2004 Car driver 20,8% Car passenger 0,8% 37,3% 12,8% Public Transport Bicycle 19,5% 8,7% Moped/ motorcycle On foot Graz: n = 931.663 trips Figure 8-6: 8.3.2 Modal split in Graz (grossed up), 2003/2004 Trip purpose and trip chaining “Trip purpose” is another variable characterising a trip, while “trip chaining” rather represents a pattern of persons’ travel behaviour. The trip purposes among the surveyed trips are well balanced, with commuting, education, leisure and shopping are the most frequently mentioned. In the SP sample the education trips are a little bit under represented for the benefit of commuting, leisure and shopping trips compared to the RP sample ( Figure 8-7). Homebound trips (e.g. from work to home) are always allocated to the last trip purpose (in case of the example it is a commuting trip). 77 6,6% 5,8% 8,2% 11,0% 21,8% 21,9% 24,7% 24,8% 27,5% 16,8% RP: n = 3.692 trips Business Figure 8-7: Commuting 23,0% 8,0% SP: n = 553 trips Shopping & Errands Education Leisure Bringing & picking up Trip purposes in Graz based on the RP and SP survey, 2003/2004 Trip chaining analyses how many trips for what kind of trip purpose are done by a person after leaving home (number of destinations per egress). Figure 8-8 presents the complex chaining between two or more trips depending on the number of destinations per egress on the basis of the SP survey. More than half of the trips of the respondents (54%) in the SP survey had only one destination before returning home, e.g. {home – work – home}, with 31% of the trips with one destination per egress are commuting trips. 26% of the reported trips are characterised by two destinations per egress, whereof 27% followed the pattern {home – shopping – shopping – home}. An example for three destinations per egress (8% of all SP trips) is {home – bringing & picking up – shopping – shopping – home} with a respective share of 19%. Four destinations per egress apply to 12% of the reported trips, whereof 44% followed the pattern {home – work – business – work – business – home}. 78 1 destination per egress 2 destinations per egress 2 destinations 26,2% 1 destination 53,7% B B 8% 3% W P W P 6% 8% 31% 2% H 10% 29% 16% H 2% 2% E 12% L 6% L 19% E 4 destinations 12,5% 7,6% S 1 destination S 2 destinations 27% 18% 3 destinations 53,7% 26,2% B B 10% P W 9% W 17% P 9% 44% H H 17% 15% 7% 10% 9% E E L L 17% 17% 19% S S 4 destinations 12,5% 3 destinations 7,6% H 3 destinations per egress Home Figure 8-8: W Work E Education 4 destinations per egress S Shopping & Errands L Leisure B Business Trip chaining in Graz based on the SP survey 2003/2004, n = 553 trips 79 P Bringing & picking up 9 Mode Choice in the Scenarios, descriptive analysis The household, person and trip information collected in the RP survey and the hypothetical mode choice and CLEVER assessment investigated in the SP survey provide the data for the analysis of the travel behaviour and requirements of potential CLEVER users. The modal shift towards CLEVER under scenario conditions – trip as well as mileage related as well as selected influencing factors on the mode choice (gender, age, trip purpose, trip length and trip chaining) – are analysed. Some of the analyses in this chapter are on a quite disaggregated level – especially in chapter 9.4. The possible level of disaggregation is always dependent on the sample size per class. The smaller the sample size gets, the larger gets the random error and the confidence intervals (compare chapter 5.3). All figures, charts and interpretations of the analysis given have to keep these circumstances in mind. The following results are related to the average travel behaviour on a weekday of the inhabitants of Graz (based on the weighted survey data). The mode choices in the scenarios refer to a corrected mode choice (compare chapter 4.5 and chapter 9.1). 9.1 Hypothetical versus realistic mode choice in the SP experiment The results of an SP experiment often leave a slight doubt about their reliability and plausibility: Are the (new) choices or decisions realistic? Are they transferable to a real life situation and result in real behaviour? Of course the researcher aims at designing an experiment or conditions (in these case scenarios) as realistic as possible, nevertheless it remains a hypothetical situation which results in a hypothetical response. It is quite easy to state in the survey “I will use the new vehicle”, but in reality one will not. These considerations brought up the idea to include questions in the SP survey to check the plausibility and validity of mode choice in the scenarios (compare chapter 4.5). In case CLEVER has been chosen – each scenario is regarded separately – these questions are checked and if not satisfactorily answered CLEVER choice is corrected to the originally chosen mode. Table 9-1 shows the hypothetical versus corrected (in terms of more realistic) mode choice in the three scenarios. The hypothetical choice of CLEVER is a very optimistic one in all three cases and accordingly reduced up to - 64% due to the plausibility check. The following analyses (mode shift and influencing factors on the mode choice) are based on the corrected mode choice. 80 Table 9-1: Hypothetical versus corrected (in terms of more realistic) mode choice in the three scenarios Modal Split SP Scenario A Relative difference real to hyp. mode choice [%] Corrected mode choice Relative difference real to hyp. mode choice [%] Corrected mode choice Relative difference real to hyp. mode choice [%] 304 337 + 11% 298 333 + 12% 284 326 + 15% - 70 25 - 64% 82 31 - 62% 86 31 - 64% Car passenger 57 56 56 0% 56 56 0% 56 56 0% Public Transport 66 59 63 + 7% 55 61 + 11% 59 65 + 10% Bicycle 52 47 49 + 4% 45 49 + 9% 51 53 + 4% Moped/motorcycle 23 17 23 + 35% 17 23 + 35% 17 21 + 24% CLEVER 9.2 Hypothetical (original) mode choice Corrected mode choice 355 Car driver Hypothetical (original) mode choice Hypothetical (original) mode choice Scenario C Revealed (original) mode choice n = 553 (trips) Scenario B Mode shift – Trip related The launch of CLEVER in Scenario A without any supporting or restrictive measures results in a modal shift towards CLEVER of about 1,4% of all trips in Graz, whereby the trips are mainly shifted from car driver trips induced by the cost advantage, while a small share comes from car passenger trips (Figure 9-1). User of public transport, moped/motorcycle or bicycle find no need or argument to use CLEVER. Car passenger 8,7% 8,4% 0,3% 37,3% 36,1% 19,6% 1,2% Public Transport 1,4% CLEVER Car driver 12,8% Bicycle 20,8% Figure 9-1: 0,9% Moped/motorcycle On foot Modal shift in Scenario A in GRAZ, 2003/2004 81 The measures favouring the use of CLEVER in Scenario B cause a slight rise of the shift from car driver trips towards CLEVER compared to Scenario A (+ 0,2 percentage points) and bring a small share of public transport passengers (0,8%) to use the new vehicle, which results in a CLEVER share of 2,4% of all trips in Graz (Figure 9-2). Cost as well as time advantages compared to the originally chosen mode make travellers use CLEVER in this scenario. Car passenger 8,4% 8,7% 0,3% 37,3% 19,6% 18,7% Public Transport 0,8% 35,9% 1,4% 2,4% CLEVER Car driver 12,8% Bicycle 20,8% Figure 9-2: 0,9% Moped/motorcycle On foot Modal shift in Scenario B in GRAZ, 2003/2004 As in Scenario C, users of private motorised vehicles (car drivers, car passengers and motorcyclists) have more than two alternatives for choice – due to the argument of rising fuel prices – the mode shift is more complex (Figure 9-3). Car passenger 8,4% 8,7% 0,3% 37,3% 35,8% Car driver 0,1% 0,8% 1,3% 0,1% 2,3% CLEVER 0,1% 20,8% Figure 9-3: 19,6% 18,9% Public Transport 12,8% 13,0% Bicycle 0,9% 0,8% Moped/motorcycle On foot Modal shift in Scenario C in GRAZ, 2003/2004 82 Car driver trips are not only substituted by CLEVER trips, but by trips made by public transport or by bicycle. The share of CLEVER trips shifting from public transport trips stays constant compared to Scenario B, which underlines the argument that time advantage is the main reason for the use of CLEVER. The share of CLEVER trips of all trips in Graz in Scenario C is 2,3%. Figure 9-4 gives an overview of the modal split in Graz in the actual state and in the three scenarios. A maximum share of CLEVER trips can be expected in Scenario B with 2,4%, and as anticipated, mainly car driver trips are substituted by CLEVER trips. On Foot 20.8% 20.8% 20.8% 20.8% Bicycle Motorcycle 12.8% 0.9% 12.8% 0.9% 12.8% 0.9% 13.0% 0.8% Public Transport 19.6% 19.6% 18.7% 2.4% 18.9% 2.3% 8.4% 8.4% 35.8% 8.7% 1.4% 8.4% 37.3% 36.1% 35.9% Actual state Scenario A Scenario B CLEVER Car passenger Car driver Figure 9-4: 9.3 Scenario C Modal split in GRAZ in the actual state and in the three scenarios, n=932.00 trips (grossed up), 2003/2004 Modal shift – Mileage related In Table 9-2 the person mileage [km/day] per mode in Graz in the actual state compared to the scenarios A, B and C are presented. The differences between the mode shift in the previous chapter and the figures in the following table are caused by the fact that the modal shift is calculated for trips and not for the mileage. Due to the shift from public transport trips towards CLEVER the total sum of person mileage of motorized modes increases in scenario B (+ 0,6%) as well as in scenario C (+ 0,4%). This fact has a significant influence on the results of the cost benefit analysis as it is based on the mileage, while the mode choice model is based on the mode choice on trip level. 83 Table 9-2: Person mileage per mode [km/day] in the actual state compared to the scenarios A, B and C for Graz (grossed up), 2003/2004 Actual state 2003/2004 Scenario A Scenario B Scenario C Person mileage per mode [km/day] Car driver 4.022.164 56,4% 3.961.624 55,5% 3.944.929 55,3% 3.943.620 55,3% 899.773 12,6% 844.184 11,8% 844.184 11,8% 844.184 11,8% 46.608 0,7% 46.608 0,7% 46.608 0,7% 39.492 0,6% Cyclist 332.059 4,7% 332.059 4,7% 332.059 4,7% 340.245 4,8% Pedestrian 200.895 2,8% 200.895 2,8% 200.895 2,8% 200.895 2,8% 1.631.319 22,9% 1.631.319 22,9% 1.600.987 22,4% 1.605.677 22,5% Car passenger Moped/motorcycle Public Transport % of total sum [km/day] % of total sum [km/day] % of total sum [km/day] % of total sum Car driver using CLEVER instead – – 60.540 0,8% 77.235 1,1% 72.785 1,0% Car passenger using CLEVER instead – – 55.589 0,8% 55.589 0,8% 55.589 0,8% PT passenger using CLEVER instead – – – 30.332 0,4% 30.332 0,4% CLEVER (sum) – – – 116.129 1,6% 163.156 2,3% 158.706 2,2% Total sum 7.132.818 100% 7.132.818 100% 7.132.818 100% 7.132.818 100% Total sum motorized 4.968.545 69,7% 4.968.545 69,7% 4.998.877 70,1% 4.986.002 69,9% Total sum nonmotorized and PT 2.164.273 30,3% 2.164.273 30,3% 2.133.941 29,9% 2.146.817 30,1% Δ Actual State – Scenario for car driver and passenger, moped and CLEVER 0,0% + 0,6% + 0,4% Δ Actual State – Scenario for cyclists, pedestrians and Public Transport 0,0% -1,4% -0,8% 9.4 Selected influencing factors on mode choice Mode shift is on the one hand dependent on the different scenario conditions – the focus is on the variables travel time and travel costs (compare chapter 11) – and on the other hand on person as well as on trip related factors. The question which people use which kind of mode for which kind of trips is reflected in the following selection, considering the dependencies of mode choice and – gender, – age, – trip purpose, – trip length and – trip chaining. 84 This chapter is limited to a two-dimensional approach, in which the correlation of two nominally scaled variables (mode choice and selected variable) is calculated via crosstabulations (using SPSS 15) and is ordinarily described by means of frequencies. The results are presented for the (weighted) RP and SP data, whereas the SP data are additionally split according to the three scenarios (based on the corrected mode choice). The mode “on foot” is only relevant for the RP data, as in the SP survey walking trips have not been considered. As already discussed above, the level of disaggregation, dependent on the sample size per class, is important for the analysis and interpretation of the results. That explains the differences of the significance levels of the RP and the SP data. Due to the larger sample size of the RP survey the correlations between mode choice and the selected variables are nearly always highly significant, while the significance levels for the SP data and especially affecting the choice of CLEVER in the scenarios are rather weak. That is because the number of chosen CLEVER trips (after all corrected ones) is simply too low in order to be able to come to a significant conclusion. The significance level in the analysis is assessed on the basis of the standardized residuals, indicating the difference between the observed and the expected frequencies, according to Table 9-3. Table 9-3: Thresholds of standardized residuals and corresponding significance levels for the analysis via crosstabulations (adapted according to ZÖFEL P. (2002)) Standardized residuals Significance level > 1,9596 significant (p < 0,05; *) > 2,5768 very significant (p < 0,01; **) > 3,2909 highly significant (p < 0,001, ***) The identification of possible relations between mode choice and selected variables via crosstabulations is a preliminary stage for the discrete choice model as well, in which more complex correlations not only between two variables but also interactions between two and more influencing factors are considered and calculated (compare chapter 11). 9.4.1 Mode choice and gender The modal split related to gender in the actual state as well as in the three scenarios in Graz shows that nearly half of all trips made by men are car (driver) trips, whereas the mode choice of women is more balanced; they make their trips quite equally on foot, by car and by public transport (Figure 9-5 and Annex: Table 16-1). A closer look at the mode shifts towards CLEVER in the three scenarios shows that in Scenario A men and women choose CLEVER to an equal share. However, men only substitute car driver trips by CLEVER trips, while women shift from car driver as well as from car passenger trips. In Scenario B, men act the same way as in Scenario A, which is indicated by the identical modal split in both scenarios. Women make use of their chance to save time using CLEVER and substitute public transport as well as car (driver) trips by CLEVER, which results in a higher share of CLEVER trips made by women (3,5%) than by men (1,4%). 85 In Scenario C, CLEVER trips made by men are reduced to 1,2% due to the rising fuel prices, which also affects the costs of CNG and induce some men to return to the originally chosen car, which explains the increase of male car driver trips compared to Scenario B. Men also substitute car driver as well as motorcycle trips by bicycle in this scenario. The share of CLEVER trips made by women stays constant while car driver trips further decrease for the benefit of public transport and bicycle trips. On foot 17.1% Bicycle 14.5% Motorcycle 1.2% Public Transport 15.1% CLEVER 17.1% 24.5% 1.2% 15.1% 12.3% 47.0% male male Actual state Figure 9-5: 22.3% 1.4% 5.0% 1.1% 15.1% 1.2% 5.0% 11.4% 0.5% 22.5% 1.4% 3.5% 3.5% 11.8% 11.8% 11.8% 45.9% 45.6% 26.7% female 24.5% 14.6% 11.2% 0.5% 1.2% 15.1% 45.6% 27.6% Car driver 14.5% 11.2% 0.5% 23.9% 1.4% 5.0% 23.9% Car passenger 17.1% 24.5% 14.5% 11.2% 0.5% 5.0% 17.1% 24.5% 26.3% female male Scenario A female Scenario B 25.8% male female Scenario C Modal split related to gender in Graz in the actual state and in the three scenarios, n = 932.000 trips (grossed up), 2003/2004 The analysis of crosstabulations (Table 9-4) reveals that men rather use the car, a moped/motorcycle or a bicycle for their trips, whereas women are primarily car passengers or public transport users. While the significance levels for the RP data are always high, due to the reasons discussed above, the significance of the SP data (including the scenarios A, B and C) is only guaranteed for the cases “car passengers” and “public transport”, primarily used by female users, whereas “moped/motorcycle riders” are more often male”. The correlation between the use of CLEVER and gender in the scenarios is not significant (Table 9-5). Table 9-4: Mode choice and gender in Graz in the RP and SP survey – key figures of the crosstabulation (weighted), 2003/2004 Modal split against gender in GRAZ in the RP and SP survey Survey Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle Pearson`s Asymptotic Chi-square significance On Foot N (trips) 5.765 446,617 (df = 5) 0,000 507 69,687 (df = 4) 0,000 Standardized residuals and significance level RP SP male 8,87 *** - 6,70 *** - 7,26 *** 3,14 *** 4,07 *** 4,34 *** female -8,96 *** 6,77 *** 7,33 *** - 3,17 *** - 4,11 *** - 4,29 *** male 1,35 ns - 3,62 *** - 2,37 * 3,09 ** 0,19 ns – female - 1,55 ns 4,15 *** 2,72 ** - 3,54 *** - 0,22 ns - * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 86 Table 9-5: Mode choice and gender in Graz according to the three scenarios – key figures of the crosstabulation (weighted), 2003/2004 Modal split against gender in GRAZ according to the scenarios Levels of attribute Scenario Car driver Car passenger Public Transport Moped/ motorcycle Bicycle CLEVER N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level Scenario A Scenario B Scenario C male 1,3 ns -3,5 *** -2,7** 3,1 ** 0,2 ns 0,3 ns female -1,5 ns 4,1 *** 3,1 ** -3,5 *** -0,3 ns -0,4 ns male 1,5 ns -3,5 *** -2,5 * 3,1 ** 0,2 ns -0,5 ns female -1,7 ns 4,1 *** 2,9 ** -3,5 *** -0,3 ns 0,5 ns male 1,5 ns -3,5 *** -2,6 ** 3,0 ** 0,3 ns -0,3 ns female -1,7 ns 4,0 *** 2,9 ** -3,4 *** -0,3 ns 0,4 ns 506 71,968 (df = 5) 0,000 506 71,524 (df = 5) 0,000 506 70,604 (df = 6) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 9.4.2 Mode choice and age The relation of mode choice and age is shown in Figure 9-6 and Annex: Table 16-2 for the actual state in Graz and in Figure 9-7 for scenario C. It is not surprising that the majority of car drivers can be found in the age group of 26 – 65 years; younger persons either own a driving licence less frequently or have no car available and hence are dependent on other modes, while seniors often quit driving because of their health. Otherwise it is remarkable that in scenario C (Figure 9-7) CLEVER users are identified to belong to the person group aged 46 and older. On foot Bicycle Motorcycle Public Transport 24,5% 17,3% 20,3% 28,4% 15,7% 10,0% 0,2% 14,0% 0,3% 3,0% 13,8% 16,4% 5,3% 7,7% 7,0% 27,3% 30,7% 10,2% Car passenger 47,5% 45,4% 15,4% 27,2% Car driver 12,5% 16 – 25 years Figure 9-6: 46 – 65 years 26 - 45 years > 65 years Modal split related to age in Graz in the actual state, n= 932.000 trips (grossed up) , 2003/2004 87 On foot 17.3% 20.3% 24.5% 28.4% 15.8% Bicycle Motorcycle Public Transport CLEVER 0.2% 0.2% 14.0% 3.0% 8.1% 13.8% 13.9% 0.9% 4.7% 4.9% 27.3% 7.7% 30.7% 5.7% 0.0% Car passenger 15.4% Car driver 12.5% 10.2% 47.2% 43.0% 20.3% 16 – 25 years Figure 9-7: 10.0% 46 – 65 years 26 - 45 years > 65 years Modal split related to age in Graz in Scenario C, n = 932.000 trips (grossed up) , 2003/2004 Analysis of the crosstabulations for the RP data supports the assumptions above (Table 9-6), while the correlation between the use of CLEVER and age in the scenarios is not significant (Table 9-7). Table 9-6: Mode choice and age in Graz in the RP and SP survey – key figures of the crosstabulation (weighted), 2003/2004 Modal split against age in GRAZ in the RP and SP survey Survey Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle On Foot N (trips) Pearson`s Asymptotic Chi-square significance 5.733 1.164,13 (df = 20) 0,000 508 189,957 (df = 12) 0,000 Standardized residuals and significance level RP SP < 16 years - 12,09 *** 14,56 *** 7,76 *** - 1,87 ns - 4,86 *** 3,57 *** 16 – 25 years - 10,75 *** 0,26 ns 5,42 *** 11,57 *** 7,94 *** 0,59 ns 26 – 45 years 8,93 *** - 5,95 *** - 4,29 *** - 3,13 ** 2,29 * - 5,05 *** 46 – 65 years 6,46 *** - 2,90 ** - 4,83 *** - 3,13 ** - 3,03 ** 0,73 ns > 65 years - 4,03 *** 3,90 *** 2,95 ** - 2,25 * - 5,51 *** 4,63 *** < 16 years – – – – – – 16 – 25 years - 3,63 *** - 0,59 ns 3,01 ** 11,37 *** - 2,00 * – 26 – 45 years 0,67 ns - 1,03 ns - 0,80 ns - 1,74 ns 1,44 ns – 46 – 65 years 0,60 ns 0,56 ns - 0,26 ns - 3,47 *** 0,83 ns – > 65 years 1,34 ns 1,45 ns - 1,14 ns - 1,72 ns - 2,28 * – * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 88 Table 9-7: Mode choice and age in Graz according to the three scenarios– key figures of the crosstabulation (weighted), 2003/2004 Modal split against age in GRAZ according to the scenarios Scenario Levels of attribute Scenario A 16 – 25 years -3,43 *** -0,56 ns 1,98 * 11,31 *** -1,95 ns 0,55 ns 26 – 45 years 0,84 ns -1,19 ns -0,56 ns -1,73 ns 1,42 ns -0,75 ns 46 – 65 years 0,68 ns 0,65 ns -0,02 ns -3,48 *** 0,79 ns -0,68 ns > 65 years 0,69 ns 1,55 ns -1,02 ns -1,71 ns -2,23 ns 2,22 * 16 – 25 years -3,39 *** -0,57 ns 2,11 * 11,30 *** -1,96 ns 0,14 ns 26 – 45 years 0,96 ns -1,20 ns -0,41 ns -1,73 ns 1,41 ns -1,29 ns 46 – 65 years 0,51 ns 0,66 ns -0,26 ns -3,47 *** 0,81 ns 0,32 ns > 65 years 0,75 ns 1,54 ns -0,95 ns -1,71 ns -2,23 * 1,65 ns 16 – 25 years -3,35 *** -0,57 ns 2,04 * 11,84 *** -2,04 * 0,07 ns 26 – 45 years 0,88 ns -1,20 ns -0,49 ns -2,20 * 1,57 ns -0,72 ns 46 – 65 years 0,59 ns 0,68 ns -0,11 ns -3,34 *** 0,54 ns -0,11 ns > 65 years 0,70 ns 1,49 ns -1,02 ns -1,67 ns -1,89 ns 1,50 ns Car driver Car passenger Public Transport Moped/ motorcycle Bicycle CLEVER N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level Scenario B Scenario C 502 186,880 (df = 15) 0,000 502 184,912 (df = 15) 0,000 502 194,903 (df = 18) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 9.4.3 Mode choice and trip purpose The distribution of mode choice and trip purpose shows that in the actual state in Graz business and bringing and picking up trips are mainly made by car (driver), commuter trips by car (driver) and by public transport, education trips mostly by public transport, by bicycle and on foot and shopping as well as leisure trips by car, by public transport and on foot (Table 9-8 and Annex: Table 16-3). The mode shift towards CLEVER referring to the trip purpose (Annex: Table 16-3) seems to come in Scenario A from business trips made as car passengers and from education, shopping and leisure trips made by car (drivers). In Scenario B commuter trips made by car (drivers) and by public transport are added to the CLEVER share of Scenario A. In Scenario C the relatively highest share of CLEVER trips is made of commuter (4,4%) and business trips (3,2%) (Figure 9-9), whereby the shift of CLEVER business trips comes again from car passengers, while commuter trips made by CLEVER originally were made by car drivers or by public transport. In addition to that commuter trips made by car (drivers) are not only substituted by CLEVER but by public transport and by bicycle trips. Leisure/car driver trips are also shifted to CLEVER as well as to bicycle. 89 On foot 8,9% Bicycle Motorcycle 9,4% 0,2% 9,8% Public Transport 13,4% 23,4% 28,7% 13,3% 19,0% 5,0% 0,8% 12,1% 5,6% Car passenger 21,9% 21,3% 19,5% 10,8% 0,4% 9,1% 0,8% 15,1% 2,3% 3,9% 19,7% 14,4% 35,2% 66,0% 8,8% 66,9% 49,2% 10,7% Car driver 35,7% 7,1% Business Figure 9-8: 31,7% Commuting Education Shopping & errands Leisure Bringing & picking up Modal split related to trip purpose in Graz in the actual state, n = 932.000 trips (grossed up), 2003/2004 On foot Bicycle Motorcycle Public Transport CLEVER 8.9% 13.4% 9.4% 0.2% 9.8% 3.2% 2.5% Car passenger 23.4% 28.7% 21.9% 19.0% 13.5% 5.0% 12.6% 0.6% 9.1% 16.0% 21.3% 4.4% 3.9% 2.3% 10.8% 0.4% 20.1% 2.0% 35.2% 66.0% 0.8% 15.1% 1.7% 14.4% 8.8% 66.9% 48.3% 0.7% 10.7% Car driver 33.5% 6.4% Business Figure 9-9: 29.2% Commuting Education Shopping & errands Leisure Bringing & picking up Modal split related to trip purpose in Graz in Scenario C, n = 932.000 trips (grossed up), 2003/2004 Analysis of the crosstabulations supports the assumptions above. Table 9-8 shows a significant result for the positive correlation of car driver and the trip purposes business, commuter and bringing and picking up for the actual state in Graz (RP). Public transport, moped and bicycle are mainly used for education trips, leisure trips are often done as car passenger. 90 The segmented results of the correlations of mode choice and trip purpose of the SP data (and the scenarios A, B and C, Table 9-9) are by contrast rarely significant however. As a significant example education trips can be taken, characterised by using public transport and mopeds, rather than the car, which makes sense as pupils very seldom own a driving licence and hence are dependent on other modes. Concerning the use of CLEVER in the three scenarios no significant correlation between the CLEVER choice and trip purpose can be shown. Table 9-8: Mode choice and trip purpose in Graz in the RP and SP survey – key figures of the crosstabulation (weighted), 2003/2004 Modal split againsttrip purpose in GRAZ in the RP and SP survey Survey Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle On Foot N (trips) Pearson`s Asymptotic Chi-square significance 5.728 1.017,09 (df = 25) 0,000 508 112,943 (df = 20) 0,000 Standardized residuals and significance level RP SP Business 10,55 *** - 3,21 ** - 6,26 *** - 1,15 ns 1,04 ns - 6,50 *** Commuter 8,97 *** - 6,21 *** - 0,38 ns - 1,36 ns - 1,21 ns - 6,41 *** Education -14,95 *** 1,59 ns 10,28 *** 4,06 *** 8,15 *** 2,09 * Shopping & errands - 4,07 *** - 0,19 ns 0,06 ns - 0,88 ns - 1,18 ns 6,45 *** Leisure - 1,01 ns 8,14 *** - 3,37 *** 0,20 ns - 3,45 *** 2,03 * Bringing & picking up 6,36 *** - 2,90 ** - 2,85 ** - 1,61 ns - 3,72 *** - 0,79 ns Business 1,60 ns 0,71 ns - 2,48 * - 1,82 ns - 0,79 ns – Commuter - 0,21 ns - 1,36 ns 0,17 ns - 0,07 ns 1,70 ns – Education - 3,30 *** - 0,60 ns 6,14 *** 3,73 *** - 0,21 ns – Shopping & errands 0,26 ns - 0,77 ns - 0,96 ns - 1,03 ns 1,83 ns – Leisure - 0,28 ns 2,70 ** 0,14 ns 1,02 ns - 2,77 ** – Bringing & picking up 1,79 ns - 1,55 ns - 1,66 ns - 1,22 ns - 0,41 ns – * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 91 Table 9-9: Mode choice and trip purpose in Graz according to the three scenarios – key figures of the crosstabulation (weighted), 2003/2004 Modal split against trip purpose in GRAZ according to the scenarios Scenario Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle CLEVER N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level Scenario A Scenario B Scenario C Business 1,91 ns 0,32 ns -2,42 * -1,82 ns -0,68 ns -0,90 ns Commuter -0,08 ns -1,29 ns 0,40 ns -0,06 ns 1,42 ns -0,52 ns Education -3,39 *** -0,57 ns 5,86 *** 3,72 *** -0,11 ns 0,54 ns Shopping & errands -0,07 ns -0,71 ns -0,77 ns -1,03 ns 1,82 ns 0,96 ns Leisure -0,33 ns 2,79 ** -0,22 ns 1,02 ns -2,65 ** 0,32 ns Bringing & picking up 2,02 * -1,54 ns -1,62 ns -1,22 ns -0,32 ns -1,04 ns Business 2,00 * 0,32 ns -2,37 * -1,82 ns -0,68 ns -1,19 ns Commuter -0,42 ns -1,29 ns -0,02 ns -0,06 ns 1,42 ns 1,29 ns Education -3,35 *** -0,57 ns 6,05 *** 3,72 *** -0,11 ns 0,18 ns Shopping & errands 0,05 ns -0,71 ns -0,64 ns -1,03 ns 1,82 ns 0,24 ns Leisure -0,22 ns 2,79 ** -0,08 ns 1,02 ns -2,65 ** -0,29 ns Bringing & picking up 2,08 * -1,54 ns -1,59 ns -1,22 ns -0,32 ns -1,18 ns Business 2,08 * 0,32 ns -2,39 * -1,76 ns -0,76 ns -1,27 ns Commuter -0,56 ns -1,29 ns -0,10 ns -0,64 ns 1,80 ns 1,81 ns Education -3,32 *** -0,58 ns 5,95 *** 3,95 *** -0,18 ns 0,08 ns Shopping & errands 0,04 ns -0,72 ns -0,44 ns -0,87 ns 1,63 ns 0,03 ns Leisure -0,17 ns 2,82 ** -0,13 ns 1,26 ns -2,72 ** -0,45 ns Bringing & picking up 2,14 * -1,54 ns -1,60 ns -1,18 ns -0,38 ns -1,22 ns 506 112,839 (df = 25) 0,000 506 116,060 (df = 25) 0,000 506 120,631 (df = 30) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 9.4.4 Mode choice and trip length The distribution of trip length according to mode choice in Graz shows that with rising trip length the share of car (driver) trips increases (Figure 9-10 and as an example for Scenario C Figure 9-11 and Annex: Table 16-5). The highest share of walking trips can naturally be found at a trip length less than 1 km. The optimum trip length for bicycle trips seems to be up to 3 km. The optimum scope of use of CLEVER required by the users is assumed to be up to 15 km for one trip, which corresponds with the target use in urban areas. The highest share of CLEVER trips is indicated from 3 to 5 km. The projected driving range of CLEVER is about 160 km. 92 8,3% On foot 3,1% 10,3% 21,1% 1,0% 19,6% Bicycle 2,2% 5,0% 1,1% 1,4% 67,0% 25,7% Public Transport 33,4% 16,4% 12,8% 9,6% 64,4% 67,1% 10.1 – 15 km 15.1 – 20 km 13,4% 15,1% 13,2% 10,3% 7,5% 7,5% Car passenger 0,2% 4,8% 2,8% Car driver 11,1% < 1 km 70,9% 52,9% 14,1% 37,4% 41,9% 28,5% 1.1 – 2 km 2.1 – 3 km 3.1 – 5 km 5.1 – 10 km > 20 km Modal split related to trip length in Graz in the actual state, n = 932.000 trips (grossed up), 2003/2004 8.3% On foot 2.2% 3.1% 10.3% 21.1% Bicycle 1.4% 67.0% 17.0% 14.6% 6.4% 0.6% 16.4% 0.3% 13.4% 3.6% 11.4% 3.0% 9.6% 29.0% 25.7% CLEVER Car driver 25.7% 2.6% 12.8% 13.2% 6.3% 1.0% Public Transport 0.2% 4.1% 1.1% 17.6% 25.7% Motorcycle Car passenger 5.3% 0.8% 1.0% 19.6% 1.7% 7.5% 10.3% 7.5% 61.5% 67.1% 70.9% 50.3% 0.2% 4.8% 0.5% 2.8% 35.7% 40.1% 27.7% 10.1% < 1 km Figure 9-11: 17,6% 0,2% 0,3% 1,0% 16,3% Figure 9-10: 6,4% 0,6% 25,7% 25,7% Motorcycle 4,1% 1,1% 1.1 – 2 km 2.1 – 3 km 3.1 – 5 km 5.1 – 10 km 10.1 – 15 km 15.1 – 20 km > 20 km Modal split related to trip length in Graz in Scenario C, n = 932.000 trips (grossed up), 2003/2004 A look at the analysis of crosstabulations (Table 9-10) supports the hypothesis that the number of car driver trips increases with rising trip length, which is positively correlated with a high significance. The same finding applies to car passenger trips. The optimum range of trip length for the use of public transport seems to be between 5 and 15 km, while bicycle trips are rarely longer than 5 km, which has already been stated above. The only significant correlation analysing the SP data (Table 9-11) can be found between trip length and the mode bicycle, characterising bicycle trips with an optimal length rarely longer than 5 km. For 93 the use of CLEVER in the scenarios no significant conclusion can be drawn concerning a correlation to trip length. Table 9-10: Mode choice and trip length in Graz in the RP and SP survey – key figures of the crosstabulation (weigthed), 2003/2004 Modal split against trip length in GRAZ in the RP and SP survey Survey Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle On Foot N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level RP SP 0 – 5 km -10,02 *** - 3,58 *** - 0,30 ns 0,48 ns 6,61 *** 11,07 *** 5,1 – 15 km 10,14 *** 4,39 *** 2,27 * 0,59 ns - 7,08 *** - 13,37 *** > 15 km 12,05 *** 3,14 ** - 2,61 * - 2,20 * - 7,36 *** - 10,05 *** 0 – 5 km - 1,05 ns - 1,21 ns - 0,75 ns 1,20 ns 3,67 *** – 5,1 – 15 km 0,75 ns 0,79 ns 0,92 ns - 0,40 ns - 3,28 ** – > 15 km 1,03 ns 1,30 ns 0,11 ns - 1,90 ns - 2,52 ** – 5.132 958,247 (df = 10) 0,000 506 43,748 (df = 8) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant Table 9-11: Mode choice and trip length in Graz according to the three scenarios – key figures of the crosstabulation (weighted), 2003/2004 Modal split against trip length in GRAZ according to the scenarios Scenario Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle CLEVER N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level Scenario A Scenario B Scenario C 0 – 5 km -1,10 ns -0,75 ns -0,68 ns 1,17 ns 3,53 *** -0,36 ns 5,1 – 15 km 0,90 ns 0,17 ns 0,77 ns -0,35 ns -3,14 ** 0,30 ns > 15 km 0,90 ns 1,32 ns 0,21 ns -1,92 ns -2,49 * 0,29 ns 0 – 5 km -1,09 ns -0,75 ns -0,86 ns 1,17 ns 3,53 *** -0,16 ns 5,1 – 15 km 0,83 ns 0,17 ns 0,95 ns -0,35 ns -3,14 ** 0,31 ns > 15 km 0,99 ns 1,32 ns 0,32 ns -0,16 ns -2,49 * -0,16 ns 0 – 5 km -0,99 ns -0,73 ns -0,85 ns 1,52 ns 3,27 ** -0,54 ns 5,1 – 15 km 0,66 ns 0,14 ns 0,99 ns -0,85 ns -2,75 ** 0,86 ns > 15 km 1,06 ns 1,32 ns 0,22 ns -1,85 ns -2,54* -0,22 ns 506 40,277 (df = 10) 0,000 506 40,763 (df = 10) 0,000 506 38,882 (df = 12) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 9.4.5 Mode choice and trip chaining Trip chaining is defined as number of destinations (or trip purposes) per egress. The relation between trip chaining and mode choice in the actual state as well as in the scenarios in Graz seems to be as following (Figure 9-12 and Figure 9-13, Annex: Table 16-7): The more destinations a person heads for (3 and more destinations per egress) the more he/she uses the car. The arguments for that may be on the one hand time savings by chaining trips by car, on the other hand it may be caused by the chained trip purposes (compare chapter 94 8.3.2). Shopping trips are combined very often, which may be due to the fact that supermarkets or shops are often easier accessible by car than by another mode and that it is more comfortable to transport things or goods by car than by bus or on foot. A look at the other modes shows that walking and the use of public transport for example are mainly linked to one destination/purpose. This fact may be related to trip duration – as walking and trips by public transport often take a priori more time – there is not much time left for more than one destination. The use of CLEVER seems to be limited to two destinations per egress, which may be explained by the relatively small size of the vehicle, which limits its use for certain purposes (purchase of large things, bringing and picking up of more than one person). On foot 14,9% 22,6% 21,5% 12,0% Bicycle Motorcycle Public Transport Car passenger 0,7% 14,3% 15,2% 1,2% 11,7% 22,3% 3,6% 50,1% 30,3% 1 destination per egress Figure 9-12: 15,5% 9,3% 45,6% Car driver 9,2% 0,0% 2 destinations per egress 3 and more destinations per egress Modal split related to trip chaining in Graz in the actual state, n = 932.000 trips (grossed up), 2003/2004 On foot 14,9% 22,6% 21,5% 12,0% Bicycle Motorcycle 0,7% 14,6% 15,3% 1,1% 3,2% Public Transport 21,1% CLEVER 2,8% Car passenger 9,3% 10,5% 43,5% 9,2% 0,0% 15,5% 0,0% 3,6% 50,1% 28,5% Car driver 1 destination per egress Figure 9-13: 2 destinations per egress 3 and more destinations per egress Modal split related to trip chaining in Graz in Scenario C, n = 932.000 trips (grossed up), 2003/2004 95 Analysis of the crosstabulations of the RP data (Table 9-12) supports the assumptions above. Car driver trips are highly significant and positive correlated to more than two destinations per egress, while walking and public transport trips rarely cover more than one destination. The significance of the segmented correlation between mode choice and trip chaining in the SP survey is – for all modes, including the use of CLEVER – not or only selectively existent (Table 9-13). Table 9-12: Mode choice and trip chaining (number of destinations per egress) in Graz in the RP and SP survey – key figures of the crosstabulation (weighted), 2003/2004 Modal split against trip chaining in GRAZ in the RP and SP survey Survey Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle On Foot N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level RP SP 1 destination - 6,74 *** 1,29 ns 3,72 *** 2,15 * 2,39 * 2,31 * 2 destinations 4,71 *** 3,41 *** - 3,30 *** - 0,51 ns - 0,75 ns - 4,47 *** 3 and more destinations 7,00 *** - 5,69 *** - 3,16 ** - 3,24 *** - 3,40 *** 0,49 ns 1 destination - 2,26 * 1,64 ns 2,67 ** 2,55 * - 0,54 ns 2 destinations 1,41 ns - 1,62 ns - 1,49 ns - 1,74 ns 0,84 ns – 3 and more destinations 2,19 * - 0,87 ns - 2,81 ** - 2,29 * - 0,08 ns – 5.766 255,61 (df = 10) 0,000 507 50,981 (df = 8) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant Table 9-13: Mode choice and trip chaining (number of destinations per egress) in Graz according to the three scenarios – key figures of the crosstabulation (weighted), 2003/2004 Modal split against age in GRAZ according to the scenarios Scenario Levels of attribute Car driver Car passenger Public Transport Moped/ motorcycle Bicycle CLEVER N (trips) Pearson`s Asymptotic Chi-square significance Standardized residuals and significance level Scenario A 1 destination -1,95 ns 1,61 ns 1,49 ns 2,77 ** -0,13 ns -0,30 ns 2 destinations. 1,01 ns -1,60 ns -0,80 ns -1,56 ns 0,82 ns 0,35 ns 3 and more destinations 1,90 ns -0,77 ns -1,41 ns -2,57 * -0,65 ns 0,09 ns Scenario B 1 destination -2,09 * 1,62 ns 1,34 ns 2,78 ** -0,12 ns 0,38 ns 2 destinations 1,11 ns -1,61 ns -0,69 ns -1,57 ns 0,80 ns -0,18 ns 3 and more destinations 1,99 * -0,78 ns -1,31 ns -2,58 ** -0,66 ns -0,39 ns Scenario C 1 destination -2,14 * 1,62 ns 1,34 ns 2,63 ** 0,06 ns 0,50 ns 2 destinations 1,12 ns -1,59 ns -0,68 ns -1,42 ns 0,67 ns -0,26 ns 3 and more destinations 2,05 * -0,79 ns -1,32 ns -2,49 * -0,79 ns -0,48 ns 506 37,138 (df= 10) 0,000 506 37,699 (df = 10) 0,000 506 36,647 (df = 12) 0,000 * ... significant (p < 0,05); ** ... very significant (p < 0,01); *** ... highly significant (p< 0,001); ns ... not significant 96 9.5 Excursus: Previous research on a “New motorised two-wheeler” In 1994, a quite similar project was completed in Graz [SAMMER G., FALLAST K., WERNSPERGER F. (1994)]. The focus was on a new vehicle for urban traffic – a “New motorised two-wheeler” (NM2W), which should guarentee individual mobility but with less impacts on the environment and on urban life compared to conventional cars. The objective was to explore the potential of this new vehicle and its benefits for urban traffic by means of a representative stated preference survey among the inhabitants of Graz. As there are a lot of parallels between the actual and the former research, a brief review is given on its results and a short comparison is drawn. The model for the new (until that time not existing) vehicle was the BMW C1 (a two-wheeler comparable to a motorcycle but with a roll-over bar and a restraint system, which allowed to drive without a helmet) and the Honda Canopy (a three-wheeler with an open driver cabin). The characteristics of the NM2W were specified by the project’s experts: electric or combustion engine, maximum speed: 45 km/h, no emissions in case of electric drive, 50% less exhaust gas emissions and noise than conventional cars cause, weather protection, good comfort and usability, convincing safety concept, transport capacity: driver and one passenger plus luggage, purchase costs approx. 5.000 EUR. 302 persons in 117 households were interviewed, reporting 1.304 trips. The sample was selected out of a former traffic survey in Graz with the focus on households having mainly car and motorcycle/moped trips. The substitution of car tips was the overall target of the hypothetical launch of the new city vehicle. Like in the CLEVER project the modal shift was estimated according to three scenarios. In Scenario A special infrastructure (dedicated lanes and parking places) was provided for the new two-wheeler. In addition to the measures favouring the new vehicle, in Scenario B parking management in Graz was extended and improvements for cyclists were implemented. Scenario C was expanded with additional public transport supply and with restriction zones for cars with combustion engines. The modal shift of the three scenarios resulted from the stated preference survey. In Scenario A the NM2W gained a share of 7% of all trips referring to transport modes (Figure 9-14). Graz 1994, Scenario A Car passenger 7,2% 8,6% n = 1.304 trips 0,4% 1,0% 0,3% 35,4% 18,0% 31,6% Car driver 23,8% 22,8% Figure 9-14: Public Transport 1,2% 3,5% 7,0% NM2W 1,0% On foot 18,1% 0,3% 12,6% 12,3% Bicycle 0,6% 1,0% 1,6% Moped/motorcycle Modal Shift in Scenario A in Graz 1994 [SAMMER G., FALLAST K., WERNSPERGER F. (1994)] 97 The mode shift resulted not only from the motorised modes (4,5% from car drivers, car passengers and motorcyclists) as desired, but also from the environmentally friendly modes (walking, cycling and public transport). The shift from car drivers and car passengers to public transport was caused in case a car driver changed to the NM2W and consequently his/her passenger changed to another mode. In Scenario B an urban-wide parking management was implemented, which induced a shift of car trips to the NM2W, as well as to public transport and the other modes. The share of car driver trips decreased to 28,6%, while the share of PT trips increased to 19,8% and the NM2W tips to 7,5% (Figure 9-15). Graz 1994, Scenario B Car passenger 8,6% 7,0% n = 1.304 trips 0,3% 28,6% 3,9% 1,2% 0,4% 7,5% NM2W 0,3% 0,3% 23,8% 1,1% 23,0% On foot Figure 9-15: 1,4% 18,0% 19,8% Public Transport 1,6% 35,4% Car driver 0,5% 0,2% 12,6% 12,8% Bicycle 0,6% 1,3% 1,6% Moped/motorcycle Modal Shift in Scenario B in Graz 1994 [SAMMER G., FALLAST K., WERNSPERGER F. (1994)] The restriction of cars with combustion engines in the city center was the main reason for the complex mode shift in Scenario C (Figure 9-16) The share of trips made by car drivers decreased from 35,4% to 22,6%, with most of these trips shifting to the NM2W (5,1%). The winners of that scenario were public transport (21,4%), cyclists (13,1%) and the NM2W (8,9%). The benefits of the launch of a new city vehicle in view of scenario conditions primarily affected the exhaust gas and CO2 emissions as well as energy consumption, showing a positive balance. 98 Graz 1994, Scenario C Car passenger 6,0% 8,6% n = 1.304 trips 0,5% 3,2% 35,4% Car driver 0,7% 0,5% 0,2% 0,4% 8,9% NM2W 0,4% 2,9% 3,8% E-Car Figure 9-16: 0,2% 23,8% 1,1% 18,0% 21,4% Public Transport 1,3% 5,1% 22,6% 1,7% 0,2% 0,6% 0,4% 0,4% 23,7% On foot 0,2% 12,6% 13,1% Bicycle 0,3% 1,6% 0,5% Moped/motorcycle Modal Shift in Scenario C in Graz 1994 [SAMMER G., FALLAST K., WERNSPERGER F. (1994)] A comparison with the recent research shows that the estimations of the share of the NM2W were very optimistic (compare chapter 9.2). While the share of CLEVER trips is between 1,4% and 2,4%, with shifts from car trips occurred in two scenarios, the share of NM2W trips was much higher (between 7% and 9%) with a shift from all kinds of modes. It seems reasonable to assume that the use of the NM2W was overestimated. The reasons for this are only speculative and may have been caused by the SP design, the presented scenarios and/or the very hypothetical character of the NM2W. In both cases the shift from environmentally friendly modes to a new eco city car must not be an alternative, as it compensates its positive effects for the environment and for urban life. 99 10 Barriers and opportunities for the choice of certain modes Beside subjective reasons and motivation of individuals for mode choice, the question here refers to the potential of alternative modes, their barriers and opportunities. They will be identified in the following on the basis of a descriptive analysis of car drivers and their trips specified in the SP survey. This analysis does not make a claim to be representative for all transport users but depicts examplarily statements of members of households with car ownership. A number of arguments concerning barriers and constraints influencing decision making can be subsumed under “Availability, knowledge and willingness” as part of external and internal constraints influencing mode choice (Figure 10-1). Availability, Feasibility Knowledge, Awareness Willingness Mode choice Figure 10-1: External and internal constraints influencing mode choice [diagram according to SAMMER G. et al. (2006) and KUPPAM A. R. et al. (1999)] Availability and feasibility includes the availability of modes in general as well as individual and external constraints: – Availability of modes (e.g. ownership of a car or bicycle, possibility to take a lift, PT supply – regional and temporal) – Individual constraints (e.g. holding a driving licence, ability to ride a bicycle, monetary resources) – External constraints regarding infrastructure (e.g. car parking at destination, cycle path or route) – Trip length (e.g. cycling or walking may be infeasible for a long distance commuter). Knowlegde and awareness respectively subsume information about alternative modes. E.g is the car driver informed about PT supply for his/her car driver trips? Does s/he know the cycling infrastructure in his/her surroundings? Isn’t it possible that a neighbour or colleague uses the same route every day and car sharing would be an option? “Naturally, transport users who frequently use certain means of transport are more familiar with their characteristics. Therefore, their subjective perception of these modes of transport is bound to correspond closely to reality. On the other hand, it could be observed that frequent car users were unable to indicate the nearest stop of public means of transport [SAMMER G. et al. (2006)].” 100 Knowledge and availability are strongly linked and should be re-checked, because people often suppose that they have no alternatives available, but in fact they do not know about them. Willingness is not least a crucial force for mode choice including perceptions, attitudes, values and preference – according to KUPPAM A. R. et al. (1999) the key to travel behaviour. It does not matter if a car driver has an attractive PT supply available and s/he is as well informed about it, s/he will not use it, if s/he is not willing to. On the other hand there are captive travellers, who might be willing to use an alternative but have no opportunity to make a choice. The perception of alternatives is obviously linked to the experience one has had with alternative modes: “Transport users who never or rarely use a certain means of transport or have not done so for a long time, are either objectively unaware of the characteristics of this alternative means of transport or have as a rule a negatively distorted perception of it. Many surveys show that there is a tendency to rate the characteristics of the alternative lower than those of the chosen mode of transport in order to confirm the appropriateness of one’s own choice [SAMMER G. et al. (2006)].” Availability, knowledge and willingness of car drivers regarding alternative environmentally friendly modes and the potential of CLEVER will be examined below. 10.1 Car availability and drives for car choice As the focus of the SP survey is on car driver trips, it is obvious that car availability expressed in number of cars per household and/or per person (Figure 10-2) as well as the individual (physical) car availability (Figure 10-3) is quite high; which means that these results are not representative for all tranport users in Graz but characterise the SP sample. The percentage of persons owning no car (33%) slightly differs to those persons, who have no car availabe (39%), which means that owning a car is not equivalent with having a car available and vice versa – although a look at the comments shows that the most frequent argument for car availability is ownership or car sharing with family members. The reasons for having no car available are beside the physical unavailability often linked to the barrier of having no driving licence (Figure 10-3). 1,4% 0,7% 4,9% 16,4% 33,1% 49,3% 32,9% 1 car 0 car 61,3% 2 cars 3 cars 2 cars 4 cars 3 cars n = 73 households n = 142 persons Number of cars per household Figure 10-2: 1 car Number of cars per person Number of cars per household and per person, results of the SP survey in Graz 2003/2004 101 - "I am the owner of this car." 33,1% - "It is the car of another member of the household." 28,9% - "I have a car of my own." 4,9% 39% - Car never available 13,4% - "I use it for various trip purposes." 47% + Cars always available - "I have no driving licence." 9,2% 14 % Car occasionally available 4,9% - "I never drive a car." n = 142 persons - Other responses. 5,6% n = 142 persons Availability of cars 0% 10% 20% 30% 40% Comments concerning the availability of the first car in the household Figure 10-3: Availability of cars per person plus comments, results of the SP survey in Graz 2003/2004 The car drivers’ willingness to shift to another mode is strongly linked to their arguments favouring the car, which is shown examplarily for scenario C in Graz as a result of the SP survey (Figure 10-4). The most frequently mentioned arguments for using the private car are “subjective advantages”, which include “functionality”, “comfort” and “flexibility”, “transport of persons or goods” and “cost advantages” compared to public transport. Especially those subjective motives are hard to overcome, when trying to induce car drivers to shift to another mode. 45% Percentage of reasons for car choice Arguments for car choice n= 253 35% 34% 25% 22% 21% 15% 12% 8% 1% 1% No parking problems Too far to use another mode 5% Figure 10-4: Company car, business trip Transport of goods, persons Subjective advantages Cost advantage Time advantage 0% Reasons of car drivers for the car choice in Scenario C in Graz (n = 253 positive mentions), 2003/2004 102 10.2 Potential of environmentally friendly modes 10.2.1 Public Transport The subjective PT availability of car drivers asked in the SP survey can be specified with 57% “never”, 32% “occasionally” and only 8% “always” available (Figure 10-5). This view is supported by the question about PT ticket ownership among all interviewed persons, where the majority (61%) has no PT ticket (which is valid one week or longer) available. These figures are only valid for the SP sample and are not representative for all transport users in Graz. 3%n.s. 8% + PT always available 14% n.s. 32% PT occasionally available 25% yes 61%no 57 % - PT not available n = 95 persons (with car driver trips) n = 142 persons PT ticket Pubilc transport supply Figure 10-5: Car users’ availability of PT concerning (subjective) PT supply per person and ownership of a PT ticket, results of the SP survey in Graz 2003/2004 Beside subjective availability of PT for car drivers, knowledege of the actual PT supply is of vital importance for a possible change from car to public transport. To check the knowledge of the interviewed car drivers each (car driver) trip had to be explained using public transport instead, specifying the PT line, name of the stop at origin and at destination and the estimated PT travel time. The analysis has shown that only a quarter of the car driver trips has been allocated properly, while in 68% of the cases car drivers were not informed or not aware of the possibilities (Figure 10-6). The estimations of PT travel time reflect moreover the image of PT – for more than 70% of the estimated trips PT travel time has been overestimated, which means that the interviewees believed that the trip with PT takes actually longer than in reality (Figure 10-6). 103 50% 46,3% m = 6,36 min s = 20,40 40% 30% 25% well informed 2% no PT supply, wrong 2% n = 355 car driver trips (only 80 trips valid) Underestimation: trip with PT takes actually longer than expected Overestimation: trip with PT takes actually shorter than expected 20% 5% 68% not informed 5% no PT supply, right 10% n = 355 car driver trips 13,8% 11,3% PT knowledge 12,5% 12,5% 11 - 20 min > 20 min 3,8% 0% < -10 min -10 to -1 min 0 min 1 - 10 min Difference of estimated minus actual travel time with public transport Figure 10-6: PT knowledge concerning actual PT supply and estimation of PT travel time related to car driver trips, results of the SP survey in Graz 2003/2004 The willingness of car drivers to change to PT (“Is it in principle imaginable for you to use public transport for this trip?”) has a clear negative tendency (Figure 10-7). The arguments against PT vary from an unsatisfactory (23,4%) or non available PT supply (21,4%) to arguments in favour of the car specified by subjective preferences and attitudes (“conveniences and comfort of car”) (27,8%). Subsuming all those arguments and answers the chance to replace car trips by public transport might be very low. 23,4% 14,1% - no PT supply - spatial 7,3% 1% n.s. 4% yes - no PT supply - temporal - no information about PT supply 4,5% 10% possibly 16,3% - conveniences and comfort of car 11,5% 86% no - subjective motives (e.g.PT dislike) 6,2% - transport of baggage and persons - car use for business trips 5,4% n = 355 car driver trips PT instead of car? 9,3% - other reasons n = 355 car driver trips 2,0% 0% - PT supply not satisfactorily (route) 5% 10% 15% 20% - not specified 25% Reasons against PT and/or in favour of car driving Figure 10-7: Willingness to use PT instead of car plus reasons related to car driver trips, results of the SP survey in Graz, 2003/2004 10.2.2 Car passenger Availability of taking a lift has been similarily assessed by car drivers as PT availability – 52% indicated that they had no possibility, 37% had an occasional and only 6% a permanent availability of taking a lift (Figure 10-8 representing the SP sample). The main argument against being a car passenger is traced back to individual attitudes favouring the car (as driver) (53,7%), which directly leads to the question about the willingness to change from car 104 driver to car passenger. 69% of the interviewed car drivers said that they could not imagine to take a lift instead of driving oneself (Figure 10-9), argueing that they would have no possibility to change (43,7%), which is again mixed up with the question about availability. - "I never take a lift. I always drive myself." 53,7% 6% + Lift always possible 5%n.s. - "I am seldom a car passenger." 13,7% 12,6% - "I often take a lift with friends or family members." 12,6% - "I am usually car passenger at the weekend or for holiday trips." 37% Lift occasionally possible 52 % - no possibility to take a lift 1,1% n = 95 persons (with car driver trips) Availability of taking a lift - other reasons n = 95 persons with car driver trips 6,3% 0% 10% 20% 30% 40% 50% - not specified 60% Comments concerning the availability of taking a lift Figure 10-8: Availability of taking a lift per person (with car driver trips) plus comments, results of the SP survey in Graz, 2003/2004 43,7% 28,7% 1% n.s. 13% possibly - If I had the possibility to take a lift ... - Subjective motives for car use 12,7% 17% yes - No possibility to take a lift - Car use for business trips 5,9% 3,1% - Conveniences and independence 2,8% - Transport of baggage and 69%no 1,7% n = 355 car driver trips - Other reasons n = 355 car driver trips 1,4% - not specified Car passenger instead of car driver? 0% 10% 20% 30% 40% 50% Reasons against taking a lift and/or in favour of car driving Figure 10-9: Willingness to take a lift instead of driving the car plus reasons related to car driver trips, results of the SP survey in Graz, 2003/2004 10.2.3 Bicycle Availability of bicycles among car drivers can be roughly compared with car availability. 44% of the interviewed car drivers have always a bicycle available, 25% occasionally and 31% have no possibility to use one (Figure 10-10). Comments concerning bicycle availability refer rather on the reasons for cycling than for availability itself. Cycling is mainly considered as an end in itself (especially for sports) and not as a mean to an end – in this case for travelling. This attitude also appears in the car drivers’ willingness to use a bicycle instead of a car for their trips. For 83% of the car driver trips cycling is no option, with the trip length (“distance 105 too far”, 23,4%) is the main argument against cycling, followed by reasons favouring the car (“conveniences and comfort”, 17,7% and “transport of persons and baggage”, 14,9%) (Figure 10-11). The potential of cycling instead of car driving has to be rated very low. - "I use my bike for sports." 24,2% - "I use my bike for leisure trips." 23,2% - "I use my bike for various trip purposes." 9,5% 18% no bicycle existing - "I occasionally use my bike." 10,5% 44% + Bicycle always available 13% - Bicycle never available - "I never ride a bicycle." 9,5% 25 % Bicycle/cycling occasionally available/possible - "There is no bicycle existing in the household." 17,9% n = 95 persons (with car driver trips) n = 95 persons with car driver trips 5,3% Availability of bicycles 0% 10% 20% - other responses 30% Comments concerning the availability of cycling Figure 10-10: Availability of bicycles per person (with car driver trips) plus comments, results of the SP survey in Graz, 2003/2004 23,4% 4,5% - unconveniences of cycling (e.g. weather) 3,7% 11% possibly - dislike of cycling and non availability 6% yes 9,0% - cycling for fun and health 8,2% - cycling only by fair weather conditions 17,7% - conveniences and comfort of car 14,9% - transport of baggage and persons 6,5% 83% no - car use for business trips 3,9% - subjective motives for car use 3,7% n = 355 car driver trips - trip distance too far - other reasons n = 355 car driver trips 4,5% - not specified Bicycle instead of car ? 0% 5% 10% 15% 20% 25% Reasons against cycling and/or in favour of car driving Figure 10-11: Willingness to use the bicycle instead of the car plus reasons related to car driver trips, results of the SP survey in Graz, 2003/2004 10.3 CLEVER View 10.3.1 CLEVER Assessment and User Requirements The characteristics of the CLEVER vehicle have been assessed by all respondents (CLEVER users and Non-CLEVER users) of the SP survey. As a result the CLEVER idea and the low running costs are mainly assessed positively, whereas the capacity of CLEVER to transport baggage or persons as well as the purchase costs (EUR 9.000,– “too 106 expensive”) are rated rather negatively (Figure 10-12). The view on the aesthetic design is quite divided. While some of the respondents found it “innovative” and “great”, the others disliked the appearance of the vehicle arguing that it is “too modern” or “too unusual”. The technical features of CLEVER are mostly positively assessed, whereby the low emissions of CLEVER are especially favoured (Figure 10-13). GRAZ n=134 100% 16% 33% 12% 44% 19% Percentage of CLEVER assessment 9% 80% 14% 39% 25% 60% -2 31% 55% 44% 40% 34% -1 +1 35% +2 37% 29% 20% 25% 20% 7% 0% CLEVER idea CLEVER design Transport persons 20% 24% 2% 2% Transport baggage Purchase costs 24% Running costs Figure 10-12: CLEVER assessment according to its practical characteristics by all the respondents (CLEVER users and Non-CLEVER users) in GRAZ, 2004 [+2 … very positive, -2… very negative] GRAZ n=134 100% 19% 13% 15% 17% 16% 12% 4% Percentage of CLEVER assessment 80% 16% 15% 17% 17% 11% 33% 60% -2 -1 43% 53% 40% +1 54% 53% 62% +2 51% 20% 24% 18% 14% 14% 11% CNGConsumption Tilting 0% Speed Acceleration Driving range Emissions Figure 10-13: CLEVER assessment according to its technical characteristics by all the respondents (CLEVER users and Non-CLEVER users) in GRAZ, 2004 [+2 … very positive, -2… very negative] 107 Imaging the hypothetical use of CLEVER, 17% of the respondents in Graz answered that they would probably use it. They argued that they were interested and curious to drive it, it would be cheaper to use CLEVER than a car for a trip and they expected an ease of parking problems in the city (Figure 10-14). Many of the respondents find it useful to substitute short car trips by CLEVER. 35% GRAZ n = 33 25% 18% 18% 15% 3% 6% 6% for leisure trips 3% for short trips no parking problems cost advantages interest, fun 0% time advantages 5% for shopping trips 12% environmental considerations Percentage of reasons for the use of CLEVER 33% Figure 10-14: Reasons and arguments for the use of CLEVER by all respondents (CLEVER users and Non-CLEVER users) in GRAZ, 2004 The majority of the respondents in Graz (59% – the rest was indifferent) could not imagine to use CLEVER. The (small) size of the vehicle was the main argument against CLEVER, followed by favouring other modes and a dislike of the design (Figure 10-15). Those persons having a negative perception of the new vehicle are not supposed to shift from car to CLEVER. 35% 25% 21% 18% 15% 14% 9% 9% 9% 7% 5% 4% other modes favoured unsafe 2% problematic for elderly people have no driving licence no interest no comfort too expensive design disliked too small unfunctional 2% not suitable for long distances 3% 1% 0% unrealisable, no market Percentage of reasons against the use of CLEVER GRAZ n = 139 Figure 10-15: Reasons and arguments against the use of CLEVER by all respondents (CLEVER users and Non-CLEVER users) in GRAZ, 2004 108 10.3.2 Market potential of CLEVER A comparison of the hypothetical (“Can you imagine to use CLEVER”) with the actual use (choice in one of the three scenarios related to the concrete trips made by the respondents) of CLEVER shows that only 3,7% of the respondents would choose CLEVER (Figure 10-16), which corresponds quite well with the corrected CLEVER choice (related to trips not per person!) in the scenarios after the plausibility check (compare chapter 9.1 and chapter 9.2). Percentage of hypothetical and actual use of CLEVER Although the high purchase costs have ben criticized, most of the persons who would like to use CLEVER would buy it (Figure 10-17). However, it is noticeable that purchase is only one type of considered availability in addition to rental or sharing. Those who answered they would like CLEVER to be given as a gift are not considered being potential CLEVER users (compare chapter 4.5). Due to its small and compact size, it is expected that CLEVER will be acquired predominately as a second or third car, which corresponds with the perceptions and answers of the respondents (Figure 10-18). 100% GRAZ n = 134 96,3% hypothetical use 80% plausible use 60% 59,0% 40% 23,9% 20% 17,2% 3,7% 0% possibly yes no Figure 10-16: Hypothetical and plausible use of CLEVER in Scenario C in GRAZ, 2004 Percentage of types of availability of CLEVER 60% 40% GRAZ n = 31 45% 20% 23% 19% 13% 0% 0% Purchase Car Sharing Gift Rental Leasing Figure 10-17: Type of hypothetical CLEVER availability in GRAZ, 2004 109 Percentage of CLEVER Status 60% GRAZ n = 22 55% 40% 27% 20% 14% 5% 0% single motor vehicle second vehicle third vehicle fourth vehicle Figure 10-18: Type of hypothetical CLEVER status in GRAZ, 2004 Deriving from the calculation of potential CLEVER users – in 3,7% of the households of Graz CLEVER would be used, the assumption was made that one person per household would consequently own one CLEVER. As the number of households in Graz is about 110.000 the potential number of CLEVER vehicles in Graz is estimated to be 4.000 (Table 10-1). Related to the car ownership rate in Graz 3,5% of the cars in Graz could be potential CLEVER vehicles. Table 10-1: CLEVER potential under Scenario C in GRAZ, 2004 CLEVER potential in GRAZ Number of households 110.000* % of households – CLEVER would be used 3,7% Number of private cars 116.000** Car ownership rate [Cars/1.000 inhabitants in Graz] 515 % of CLEVER related to number of cars 3,5% Number of CLEVER in total 4.000 * [STATISTIK AUSTRIA (2005): Grosszaehlung 2001]; average household size in Graz: 2,03 persons ** [STATISTIK AUSTRIA (2005): Statistik der Kraftfahrzeuge] 110 11 Discrete Mode Choice Model 11.1 Modelling Assumptions “In order to develop models capturing how individuals are making choices, we have to make specific assumptions about: the decision maker: who is the decision maker and what are his/her characteristics; the alternatives: determine possible options of the decision maker; the attributes: identify the attributes of each potential alternative that the decision maker is taking into account to make his/her decision; the decision rules: they describe the process used by the decision maker to reach his/her choice [BIERLAIRE M. (1997)].” Additionally assumptions are made about information and knowledge of the decision maker according to SAMMER G., HÖSSINGER R. (2008) as well as about a possible methodological influence. 11.1.1 Decision maker and trip characteristics “Choice models are referred to as disaggregate models. It means that the decision maker is assumed to be an individual (not restrictive – can also be a group of persons like a household etc.). Because of its disaggregate nature, the model has to include the characteristics, or attributes, of the individual like age, gender, eyes colour etc. The analyst has to identify those that are likely to explain the choice of the individual. There is no automatic process to perform this identification. The knowledge of the actual application and the data availability play an important role in this process [BIERLAIRE M. (1997)].“ In this case the decision maker is limited to the group of car drivers. As car driver trips represent the majority of the SP sample (64,2%) this sampling fraction has been selected for further analysis. “Gender” and “age” are the most obvious individual attributes, which are available. 62,8% of the car driver trips in the SP sample are made by men, 37,2% by women. Age distribution shows that slightly more than 50% of the car driver trips are made by the age group 26 – 45 years, followed by age 46 – 65 years (Figure 11-1). As the analysis is not primarily based on the decision maker him/herself but on the individual trips, some crucial trip related attributes are presented as well at this point. Those include for example “trip length”, “trip purpose” and “trip chaining”. The majority of the trips are commuter, leisure or shopping trips with a length of 3 to 10 km and one destination per egress (Figure 11-1). For the trip based model the characteristics of the decision maker as well as those of the trip itself are allocated to each single trip (compare also chapter 9.4). 111 Trip length (n = 347.427 car driver trips) Age (n = 347.427 car driver trips) > 65 years m = 47 years s = 1,325 7,3% 46-65 years s = 0,823 3,3% 6,7% 10,1 - 15 km 34,3% m = 10,1 km 12,5% > 20 km 15,1 - 20 km 25,1% 5,1 - 10 km 20,8% 3,1 - 5 km 51,2% 26-45 years 13,2% 2,1 - 3 km < 25 years 11,6% 1,1 - 2 km 7,2% 6,9% < 1 km 0% 10% 20% 30% 40% 50% 60% 0% Trip purpose (n = 347.427 car driver trips) Bringing & picking up Leisure 7,9% 5% 10% 15% 20% 25% 30% Trip chaining (n = 347.427 car driver trips) Business 3 destinations per egress 13,3% 1 destination per egress 26,5% 23,3% 48,9% 30,5% 24,7% 22,3% Shopping & errands Commuter 2 destinations per egress 2,7% Education Figure 11-1: Selected characteristics of car driver trips (SP sample weigthed and projected) 11.1.2 Alternatives “Analyzing the choice of an individual requires the knowledge of what has been chosen, but also of what has not been chosen. Therefore assumptions must be made about options, or alternatives, that were considered by the individual to perform the choice. The set containing these alternatives, called the choice set, must be characterized. … The choice of transportation mode is a typical application leading to a discrete choice set (contrary to a continous choice set). A discrete choice set contains a finite number of alternatives that can be explicitly listed [BIERLAIRE M. (1997)].” Two concepts of choice sets can be distinguished: the universal choice set and the reduced choice set. “The universal choice set contains all potential alternatives in the context of application.The reduced choice set is the subset of the universal choice set considered by a particular individual. Alternatives in the universal choice set that are not available to the individual under consideration are excluded (for example, the alternative car may not be an option for individuals without a driving license). The awareness of the availability of the alternatives by the decision maker should be considered as well [BIERLAIRE M. (1997)].” The present mode choice models are based on a reduced choice set (compare chapter 7.2.3). The number of potential alternatives is constrained by the definition of the scenarios. While in Scenario A and B only two potential alternatives are presented – CLEVER and the actually chosen mode (stated in the RP survey), in Scenario C up to 7 alternatives (including the possibility to skip the trip, which in fact has never been chosen in the SP survey) are 112 offered to car drivers, car passengers and motorcyclists due to the altered scenario conditions. According to this, availability and awareness of availability of alternative modes is especially relevant when modelling mode choice in Scenario C. Concerning CLEVER, it is assumed that the presentation and characterisation of the new car satisfies the decision makers’ needs for information. Otherwise it is not guaranteed that s/he really gets aware of the hidden consequences the choice of CLEVER might entail in reality e.g. sale of the prime car or additional car with additional costs; it is a moot question to what extent these constraints are really considered in the hypothetical decision process or whether they are part of the unobserved influences. “The choice set includes the alternatives that are both feasible to the decision maker and known during the decision process. The feasibility of an alternative is defined by a variety of constraints such as physical availability (e.g. the availability of a bus service between the commuter’s home and place to work), monetary resources (e.g. a taxi fare may be unaffordable to a low-income worker), time availability (e.g. the walk mode may be infeasible for a long-distance commuter), informational constraints (e.g., lack of knowledge about the bus service), and so on [BEN-AKIVA M., LERMAN S.R. (1985), p. 33/34].” Focusing on car driver trips, constraints may appear concerning the alternatives “car passenger”, “PT”, “bicycle” and “walking” in Scenario C. Limiting factor for “bicycle” and “walking” is apparently the trip length. Availability affects the alternative “car passenger”, while “PT” is not only dependent on its availability but also on the awareness of availability. Data availability and real choice of alternatives in Scenario C influence the consideration of those constraints in the model. In fact only four alternatives are available at most for car driver trips in Scenario C (Table 11-1). Table 11-1: Segmentation in number of alternatives for car driver trips in Scenario C Number of alternatives Alternatives 2 alternatives: Car driver – CLEVER 3 alternatives: Car driver – CLEVER – PT 35 Car driver – CLEVER – bicycle 28 4 alternatives: Number of car driver trips 1 Car driver – CLEVER – PT – bicycle Sum of car driver trips: 291 355 Another constraint results from the SP design itself and its trip based mode choice. In the survey the respondent (= decision maker) made his/her mode choice for each of his/her trips. Although trips and mode choice respectively are treated independently, there are of course constraints concerning trip chaining. In the normal case the user is captured to choose the same mode, which has been used for the home based trip, for the return trip as well – the decision maker is not totally free anymore to choose another alternative. In the discrete choice analysis all trips, irrespective if they are home based or return trips, are used equally after checking the consistency of mode choice, to keep a critical number of trips for modelling. 113 11.1.3 Attributes of alternatives “Each alternative in the choice set must be characterized by a set of attributes. The analyst has to identify the attributes of each alternative that are likely to affect the choice of the individual. In the context of a transportation mode choice, the list of attributes for the mode car could include travel time, the out-of-pocket cost and the comfort. Note that some attributes may be generic to all alternatives, and some may be specific to an alternative (bus frequency is specific to bus). Also qualitative attributes, like comfort may be considered. An attribute is not necessarily a directly observed quantity. It can be any function of available data. For example instead of considering travel time as an attribute, the logarithm of the travel time may be considered. The out-of-pocket cost may be replaced by the ratio between the out-of pocket cost and the income of the individual. The definition of attributes as a function of available data depends on the problem. Several definitions must usually be tested to identify the most appropriate [BIERLAIRE M. (1997)].” The alternatives in the choice sets of the SP experiment are characterized by the attributes travel time and travel costs (total costs and fuel costs). There are three attribute levels corresponding to the three scenarios. Additionally 4 interview groups have been generated to vary those two attributes (compare chapter 7.2.4.1). As the focus of modelling is on car driver trips, this segment is described in the following. Nearly 90% of the car driver trips in the SP sample last less than 30 minutes (Figure 11-2). While there is no difference between car and CLEVER trips concerning travel time in Scenario A, there is a consistent shift of the duration of CLEVER trips in Scenario B and C from the second group (11 – 20 min) to the first group (0 – 10 min). The difference between the alternatives car and CLEVER concerning travel time for a single trip, which is presented to the respondent in the SP experiment, is mostly very small (for example Scenario B: car driver: 15 min, CLEVER: 9 min), and the difference is even less with decreasing travel time (compare calculation of CLEVER travel time chapter 7.2.4.3). Car driver Scenario A, B, C B C 39,4% 39,4% 31,0% 29,3% A 31,0% 29,3% 29,3% 30% 29,3% 29,3% 2,8% 2,0% C 2,8% 2,0% 11,3% 8,7% B 2,8% 2,8% 11,3% 8,7% 11,3% 11,3% 17,2% 13,5% 10% 17,2% 13,5% 17,2% 20% 17,2% % of car driver trips 39,4% CLEVER Scenario A, B, C 39,4% 40% 44,8% 44,8% 50% 0% 0 - 10 min A B 11 - 20 min C A B C 21 - 30 min A 31 - 60 min A B C > 60 min Travel time [min] Figure 11-2: Travel time of car driver and CLEVER trips according to the three scenarios, sample: car driver trips of the SP survey in Graz 114 The range of the calculated travel costs is from 0,04 € to 80,00 € per trip, with the majority of the car driver (66%) and CLEVER (85%) trips costing less than 3,00 €. The distribution of CLEVER and car driver travel costs per scenario are depicted in Figure 11-3 and Figure 11-4. Scenario A and B present the real (calculated), Scenario C the projected (due to the assumptions of the scenario) travel costs (compare chapter 7.2.4.2). It may be important to stress that the difference between the two alternatives car and CLEVER in the choice sets is very small in the majority of cases – e.g. car driver: 0,4 €/trip, CLEVER: 0,2 €/trip. This fact may hinder a clear choice in the SP experiment and might be relevant to be considered when interpreting the results of the model. As additional information, the estimated costs for car driver trips (estimated by the respondents in the SP survey) are given in Figure 11-4 and Figure 11-5. It is noteable that nearly two-thirds of the respondents underestimate the costs of their car driver trips, only 5% are right and one third overestimates the travel costs. The knowledge – in this case the lack of knowledge – of travel costs may relativise their importance as a decision criterion for car choice. 60% 49,9% 52,7% 40% C A B 1,1 - 2 C A B 2,1 - 3 A C A B 5,1 - 10 C A B 5,4% 5,4% 3,1% B 3,1 - 5 2,5% 5,6% 9,0% C 4,8% B 0-1 5,4% A 5,9% 7,0% 0% 4,8% 28,7% 10% 3,9% 20% 26,5% 30% 27,6% % of car driver trips 51,8% Total costs CLEVER Scenario A, B, C 50% C > 10 Total costs of CLEVER trips [€] Figure 11-3: Total travel costs of CLEVER trips as an alternative for car driver trips in the three scenarios 115 60% Estimated total costs car driver Total costs car driver Scenario A, B, C C A 1,1 - 2 2,1 - 3 3,1 - 5 5,1 - 10 Estimated and projected costs of car driver trips [€] 11,3% 7,9% 3,9% B 7,9% A 12,4% C 9,3% B 9,3% A 5,7% 17,5% C 16,6% 11,1% B 19,4% A 16,6% C 14,4% B 15,9% A 14,4% C 0-1 Figure 11-4: 28,2% B 10% 0% 23,7% 32,6% A 15,8% 20% 23,7% 30,8% 30% 28,2% 40% 23,7% % of car driver trips 50% B C > 10 Estimated and projected costs of car driver trips in the three scenarios 100% Scenario A Scenario B 80% Scenario C % of car driver trips 78,1% 60% 64,4% 64,4% 40% 30,2% 20% 30,2% 21,9% 5,4% 5,4% 0,0% 0% A Trip costs ... B C underestimated A B C corresponding A B C overstimated Difference between estimated and projected costs for car driver trips Figure 11-5: Difference between estimated and projected travel costs for car driver trips in the three scenarios The two attributes “travel time” and “travel costs” are linked via, because both are dependent on, the trip length (compare calculation patterns chapter 7.2.4). Table 11-2 to Table 11-4 show the correlation of trip length and travel costs with an r of nearly 1 for car and CLEVER trips for all three scenarios (as a result of the calculation assumptions, where the trip length [km], estimated by the respondents, is directly entering the formula for calculating travel costs). The correlation between trip length and travel time on the other hand, presented in the same tables, results in a lower r between 0,7 and 0,8, which can be explained by the fact that both values have been estimated by the respondents and do not always correspond completely. While in Scenario A travel times for car and CLEVER have the same value 116 (resulting in r = 1) and originate from the estimates of the respondets, in Scenario B and C the values are computed according to the assumed calculation patterns. These conclusions result in a correlation between the trip attributes “travel time” and “travel costs” as well. Table 11-2: Correlation between attributes of alternatives and trip length in Scenario A Correlation r between attributes of alternatives and trip length in Scenario A Car driver Modes Attributes Trip length Car driver CLEVER Travel costs Travel time Travel time 0.999 0.786 0.999 0.786 1 0.786 0.999 0.786 1 0.786 1 1 0.786 Travel costs Travel time CLEVER Travel costs Travel costs 1 Travel time Table 11-3: Correlation between attributes of alternatives and trip length in Scenario B Correlation r between attributes of alternatives and trip length in Scenario B Car driver Modes Attributes Trip length Car driver CLEVER Travel costs Travel time Travel time 0.999 0.787 0.999 0.724 1 0.787 0.999 0.724 1 0.787 0.963 1 0.726 Travel costs Travel time CLEVER Travel costs Travel costs 1 Travel time Table 11-4: Correlation between attributes of alternatives and trip length in Scenario C Correlation r between attributes of alternatives and trip length in Scenario C Car driver Modes Attributes Trip length Car driver Travel costs CLEVER Travel costs Travel time Travel time 0.998 0.787 0.998 0.724 1 0.796 0.999 0.737 1 0.795 0.963 1 0.735 Travel time CLEVER Travel costs Travel costs 1 Travel time At this point the question concerning orthogonality and its relevance for modelling arises. Due to the design of the SP experiment and the calculation assumptions, which were intended to be as plausible and realistic as possible, orthogonality (no or little correlations between attribute levels) between the attributes of alternatives travel costs and travel time 117 and between the attributes of alternatives and trip length respectively is not fulfilled. What does this mean for modelling? “If we have a product with two attributes of interest, …, then it is clearly undesirable that their levels should be highly correlated in the experimental design.” … “We would then have perfect collinearity and we could only obtain a valuation for the joint presence of both attributes, and could not deduce the separate effects. As is well known, high levels of collinearity will reduce the accurancy with which the separate effects can be determined. This has led, quite sensibly, to the use of orthogonal experimental designs, by which we mean that the attribute levels are chosen such that there is zero correlation between the attributes [FOWKES A.S. (2000)].” In the present case, examining trip length and travel costs it is indicated that it is obsolete to join both attributes in one model due to their perfect collinearity. However, modelling travel costs and travel time should be feasible despite violating orthogonality, referring to FOWKES A. S., WARDMANN M. (1988): “It is often benefical to sacrifice some purity in the experimental design (e.g. loose complete orthogonality) if one gains in realism.” They and other colleagues have gone further recently suggesting that in certain cases (i.e. if one wants to estimate parameter ratios such as the value of time) it may be preferable to design with purposely correlated attributes [WATSON S. M. et al. (1996)]. According to ROSE J., BLIEMER M. (2007) “orthogonality may not be important in estimating logit models, as it is the differences between the attribute levels that count”, “orthogonality is usually lost in the data anyway, due to missing blocks of observations or covariates (socioeconomics, such as income or gender)” and “non-orthogonal designs can yield more reliable parameter estimates”. 11.1.4 Theory of logit model The choice of transport mode is a typical application for discrete choice models. They are based on discrete choice sets (contrary to continous choice sets), which contain a finite number of alternatives that can be explicitly listed.The characterization of the choice set is done by the identification of the list of alternatives [BIERLAIRE M. (1997)]. For discrete choice SP experiments, the usual method of analysis is to model Pj(n), the probability that person n will choose mode j, within a logit formulation [WATSON S. M. et al. (1996)]. The usefulness of these models has been enhanced by the incorporation of behavioural aspects into the models using utility theory as a decision rule used by the decision maker to come up with the actual choice [ARASAN V. T. (2003)]. “In behavioural travel demand analysis, travel is modelled as a choice process. The choice process can be explained by utility theory. In this theory, trip maker’s preference towards each alternative (mode) is described by an attractiveness or utility measure associated with each alternative. The decision-maker is assumed to choose that alternative which yields the highest utility. Utilities are expressed as a sum of estimated attractiveness and a random term. This attractiveness is a function of the attributes of the alternative (such as travel time, travel cost, etc.) as well as the decision-maker’s characteristics (such as income, employment status, vehicle ownership, etc.) [ARASAN V. T. (2003)]." “The utility of any alternative is, from the perspective of the analyst, best viewed as a random variable. This leads directly to the notion of random utility models in which the probability of 118 any alternative i being selected by person n from choice set Cn is given by the following [BEN-AKIVA M., LERMAN S.R. (1985)]: P (i C n ) = Pr(U in ≥ U jn , ∀j ∈ C n ) (1) The probability that Uin = Ujn for any i and j in the choice set is ignored. Formally, if the distributions of Uin and Ujn can be characterized by a probability density function, Pr(U in = U jn ) = 0. In the following the binary choice model, where Cn contains exactly two alternatives is pursued. The choice set Cn is denoted as {i, j}, where for example alternative i might be the option of driving to work and alternative j would be using transit. The probability of person n choosing alternative i is: Pn (i ) = Pr(U in ≥ U jn ) (2) and the probability of choosing alternative j is Pn ( j ) = 1 − Pn (i ) Recalling that Uin and Ujn are random variables, each of the utilities is divided into two additive parts as follows: U in = Vin + ε in (3) U jn = V jn + ε jn where, U in = utility of alternative i for person n Vin = estimated attractiveness of alternative i for person n ε in = random part (error term) Vin and Vjn are called the systematic (or representative) components of the utility of alternative i and alternative j; εin and εjn are the random parts and are called the disturbances (or random components). It is important to stress that Vin and Vjn are functions and are assumed here to be deterministic (i.e. nonrandom). The terms εin and εjn may also be functions, but they are random from the observational perspective of the analyst. The estimated attractiveness Vin or Vjn is a function of various attributes of the alternative and characteristics of the individual. It is usually assumed as a linear function (linear-in parameters-function) of these attributes and characteristics such as: V ji = β j + β1COST j + β 2TIME j (4) where ß are the parameter estimates or coefficients and COST and TIME are the variables or in this case the generic attributes of alternatives. In logit models the estimation of the parameters is usually obtained by Maximum Likelihood Estimation. The probability that the person n chooses alternative i over all other alternatives is Pn (i ) = Pr(U in ≥ U jn ) (5) = Pr(Vin + ε in ≥ V jn + ε jn ) 119 = Pr(ε jn − ε in ≤ Vin − V jn ) The binary logit model arises from the assumption that εn = εjn – εin is logistically distributed, namely 1 1 + e − με n μe − με n f (ε n ) = (1 + e − με n ) 2 F (ε n ) = μ > 0,−∞ < ε n < ∞ (6) where μ is a positive scale parameter. Besides approximating the normal distribution quite well, the logistic distribution is analytically convenient. The assumption that εn is logistically distributed is equivalent to assuming that εjn and εin are independent and identically Gumbel (or type I extreme value) distributed. Under the assumption that ε n is logistically distributed, the choice probability for alternative i is given by Pn (i ) = Pr(U in ≥ U jn ) = = (7) 1 1+ e − μ (Vin −V jn ) e μVin e μVin + e μV jn This is the binary logit model. The multinomial logit (MNL) model is expressed as Pn (i ) = ∑ eVin j∈C n e (8) V jn One of the widely discussed aspects of the MNL is the independence from irrelevant alternatives (IIA), which can be explained as follows: The IIA holds that for a specific individual the ratio of the choice probabilities of any two alternatives is entirely unaffected by the systematic utilities of any other alternatives [BEN-AKIVA M., LERMAN S.R. (1985)].” 11.2 Data segmentation Data gained at the SP experiment can be reasonably segmented on two different levels – according to the alternatives and/or the scenarios – leading to potential variants of mode choice models (Figure 11-6). On the one hand, there are four different modes (car driver, car passenger, PT and bicycle) revealed at the RP survey and identifying the trips, on the other hand there are three scenarios A, B and C defining the trip attributes levels. Those two superior levels build up a kind of matrix yielding the possible model options. 120 For example: Modelling only the originally chosen mode (car driver, car passenger, PT or bicycle) with the alternative mode CLEVER for scenario A results in a binomial logit model (two alternatives). The same applies to the other modes and scenarios (with restrictions for scenario C). Each scenario may be treated and modelled separately or in combination with the data of the other scenarios (A and B or A and B and C) for one mode segment. This approach may result in: – 4 binomial logit models for scenario A, – 4 binomial logit models for scenario B, – 4 binomial logit models for the combination of scenario A and B, – 2 binomial logit models and 2 multinomial logit models for scenario C and – 2 binomial logit models and 2 multinomial logit models for the combination of scenario A, B and C. Modes / Alternatives Scenario A Car driver – CLEVER Car pass. – CLEVER Scenario B CLEVER – Car driver Car passenger PT Bicycle PT – CLEVER Bicycle – CLEVER Car driver – CLEVER Car pass. – CLEVER Scenario A and B: Scenario A and B: Binomial logit models (4) Binomial logit models (4) or MNL (1) or MNL (1) Scenario A and B and C: Binomial logit models (2) and CLEVER – Car driver Car passenger PT Bicycle PT – CLEVER Scenario B: Binomial logit models (4) MNL (Car driver, Car passenger) (2) or MNL (1) or MNL (1) Bicycle – CLEVER Car driver – CLEVER Car pass. PT Bicycle Scenario C Model variants CLEVER – Car driver Car passenger PT Bicycle Car pass. – CLEVER Car driver PT Bicycle Scenario C: Binomial logit models (2) and MNL (Car driver, Car passenger) (2) or PT – CLEVER MNL (1) Bicycle – CLEVER Figure 11-6: Potential variants of models according to scenarios and mode choice alternatives However, segmenting data not according to the modes but entering all trips, that means all four modes, into one model, leads to a multinomial model – with the alternatives car driver, 121 car passenger, PT, bicycle and CLEVER – for each scenario (A, B, C) or for the combined scenarios (A and B, A and B and C) respectively. This approach requires a consideration of the availability of modes for each trip (person) in the model, which in fact corresponds to the alternatives, which have been presented to the respondents in the SP survey. In this case each trip (person) has a reduced choice set, namely the originally chosen mode and the new alternative CLEVER. The question is, which of those possible models are worth to be pursued and which aspects have to be considered. Recalling the objectives of the launch of the environmentally friendly mode CLEVER confirms the focus on the segment “car driver trips”, as those trips are the ones that should preferably be shifted towards CLEVER. The selection of the sample of the SP survey is as well based on this target (compare chapter 5.1.2). That leads to the second important aspect for modelling – the sample. A critical number of cases using CLEVER instead of the originally chosen mode is important in order to gain reasonable results, which should be guaranteed with the selection of the car driver trips. Table 11-5 shows the number of cases per mode that shift towards CLEVER in the three scenarios and distinguished between hypothetical (original) and corrected (in terms of more realistic) mode choice (compare also chapter 9.1). Table 11-5: Number of cases per mode shifting towards CLEVER in the three scenarios Number of cases shifting towards CLEVER Modal Split SP Scenario A Scenario B Scenario C Shift from … Car driver Corrected mode choice Hypothetical (original) mode choice Corrected mode choice Hypothetical (original) mode choice Revealed (original) mode choice Hypothetical (original) mode choice n = 553 (trips) Corrected mode choice according to the SP survey 355 51 18 57 22 65 22 Car passenger 57 1 1 1 1 1 1 Public Transport 66 7 3 11 5 11 5 Bicycle 52 5 3 7 3 5 3 Moped/motorcycle 23 6 0 6 0 4 0 - 70 25 82 31 86 31 CLEVER (Sum of cases) The considerable difference between original and corrected mode choice can be traced back to the fact that in course of the interview the question about availability of CLEVER has been posed after the stated preference part concerning mode choice in the scenarios (Figure 11-7). Actually the respondents had to take two independent decisions, whereas in reality the decision about availability should precede the one about mode choice. To guarentee plausibility of answers and to consider the respondents’ awareness, mode choice has been revised (from the stated CLEVER choice back to the originally chosen car) resulting in the corrected choice sample 2. A combination of both data sets (sample 3) is thinkable as well and could be the data base for a model aiming at explaining the influence of some kind of awareness (compare chapter 11.4.6). 122 SP corrected mode choice for A, B and C done by analyst Availability of CLEVER Decision 2 Awareness (purchase, sharing etc.) Original + corrected mode choice for A, B and C Figure 11-7: sample 2 for A, B and C sample 3 SP original mode choice Decision 1 sample 1 Revealed mode choice per trip Flow of decisions in the SP interviews and resulting samples for modelling Following the considerations above, only the data segment “car driver trips” are modelled for the single or combined scenarios and differentiated according to the three choice samples (Table 11-6). In the course of modelling the most promising options are verified. The definite number of models is finally dependent on the selection and combination of variables. Table 11-6: Modelling car driver trips according to the scenarios and choice samples Scenario Alternatives A Car driver, CLEVER Model Binomial Logit Choice sample ntrips original ( sample 1) 355 corrected (sample 2) 355 original + corrected 710 (sample 3) B A+B Car driver, CLEVER Car driver, CLEVER Binomial Logit Binomial Logit original (1) 355 corrected (2) 355 original + corrected (3) 710 original (1) 710 corrected (2) 710 original + corrected (3) C A+B+ C Car driver, CLEVER, MNL PT, bicycle Car driver, CLEVER, MNL PT, bicycle 1.420 original (1) 354 corrected (2) 354 original + corrected (3) 708 original (1) 1.064 corrected (2) 1.064 original + corrected (3) 2.128 123 11.3 Process of modelling “The process of modelling and the search for the best model are directly linked to the selection of variables. Numerous combinations of different variable specifications increase the number of possible models in a way that an exhaustive search for the best model is usually impossible [MAIER G., WEISS P. (1990), p.169].” To ease the selection of variables and to find reasonable combinations in a manageable scope, the variables are classified and assessed concerning their importance, in order to verify the hypotheses following modelling assumptions (chapter 11.1). Finally three types of models are defined according to the selected variables, which will be further pursued. 11.3.1 Classification of variables and hypotheses The variables gained in the RP and SP survey are primarily classified according to three levels “household”, “person” and “trip” and an additional level “method”. Household variables apply to all members of one household, just as person variables (and the appropriate household attributes) stay always the same for all trips of one person. This affects the setup of the model. Modelling on trip level means that only trip attributes vary across the trips, called generic, in comparison to alternative specific (socio-economic) variables of the other three levels, which vary across the individuals (compare chapter 11.3.4). Table 11-7 lists the (survey specific) variables and their scale as well as the underlying hypotheses. Additionally, the variables are classified according to seven categories with regard to the model (the allocation of the variables in the matrix is coloured green): – Attributes of alternatives (travel time and travel costs), – trip related attributes (trip length, trip purpose, baggage etc.), – mobility pattern (number of trips per day, trip chaining etc.), – socio-demographic characteristics (age, gender etc.), – subjective attitudes, – barriers and restraints and – methodological effects. 124 Table 11-7: Classification of variables and hypotheses Household Person numerical no Household category Dummy no Household income Dummy yes Internet access Dummy no Number of cars available numerical no Gender Dummy yes Age numerical (Dummy) yes Education Dummy no Employment Dummy no Car ownership (number of cars) numerical no Time at home numerical no Time in transit numerical yes Trip chaining Dummy yes Availability of modes (car, PT) Dummy no Knowledge of PT supply Dummy no no Attitude towards mode alternatives Attitude towards CLEVER Influence on the choice of CLEVER Household size Methodical effects no Barriers and restraints Hypotheses Subjective attitudes Dummy Socio-demographic characteristics Home address Mobility pattern Classification of variables Trip related attributes Scale Attributes of alternatives Variables yes Dummy … Trip Mode choice (incl. CLEVER) dependent variable Trip purpose Dummy yes Trip length numerical yes Travel time numerical yes Travel costs numerical yes Origin/destination Dummy no Motives for (against) mode choice Dummy no Baggage Dummy yes Car passenger Dummy yes Parking at destination Dummy yes Interviewer Dummy no Number of interview numerical yes Interview duration numerical yes Method … … yes … no … Hypothesis that variable has an influence on the choice of CLEVER, Hypothesis that variable has no influence on the choice of CLEVER 125 As the aim of the mode choice model is primarily to explain behaviour, the crucial question is which of these variables and attributes have an influence on the mode (CLEVER) choice. However, this question can not be answered satisfactorily a priori, but be supported by the underlying hypotheses of priliminary selected variables outlined in the last column of Table 11-7: – Household income: The larger the income the higher is the probability to choose/buy CLEVER (because of its relatively high purchase costs). – Gender: The choice of CLEVER is independent of gender – men and women use CLEVER comparably, but have other motives. – Age: Younger people (16 – 40 years) are more attracted by the design and innovative concept of the new vehicle than older ones. – Trip chaining, time in transit: A complex trip chaining hinders the use of CLEVER as some trip purposes limit its use. Time in transit correlates with trip length; less time in transit (short trips) favours the use of CLEVER. – Trip purpose: CLEVER is mainly used for commuter and leisure trips. Bringing & picking up as well as shopping trips are only limited possible due to the size of CLEVER. Baggage and car passenger are limiting variables for the use of CLEVER. – Parking at destination is an incentive for a CLEVER use. – Trip length: Short distances favour the use of CLEVER. Though it has to be considered that trip length is implicated in travel time and travel costs. – Travel time and travel costs: In the SP experiment, travel time and travel costs are provided for all possible modes for each trip. These attributes of alternatives are the objective basis for the decision. – Attitude towards CLEVER: Positive attitudes favour the use of CLEVER. – Interview duration, number of interview: Concentration and interest of the respondents and simultaneously the probability to choose CLEVER decrease with increasing duration of the interview. However, an increasing number of interviews done by one interviewer might have a positive effect on the CLEVER choice. The latter variables on methodical effects have an exceptional position as they have no or should not have a direct influence on the mode choice but will be used to test the sensitivity of method on influencing the choice. 11.3.2 Selection of variables and types of models The selection of variables and their combination resulting in a manageable number of models is based on the modelling assumptions and underlying hypotheses to explain behaviour as well as to verify the efficiency of the scenarios. Apart from contents and hypotheses statistical criteria will be considered. “Attributes of alternatives” including travel costs and travel time are the most important variables entering the model as generic variables (compare chapter 11.3.4). The second cluster of variables includes socio-demographic data as well as measurable trip or mobility related data. They are entered as alternative specific variables. 126 Beside rational and socio-economic aspects choice models must include behavioural aspects of people [ARASAN V.T. (2003)]. “… our best econometric choice models explain only a fraction of the variation that is found in actual choice behaviour. There are many types of additional information that could be used to improve the explanatory power of the models. … Relevant types of data are those on attitudes, perceptions and personality traits [BRADLEY M. (2004), p.4].” Model 3: Subjective attitudes Model 2: Socio-demographic characteristics, Trip related attributes, Mobility pattern Reason COST and TIME, CLEVER Idea, CLEVER Design GENDER, AGE, INCOME; (LENGTH), (PURPOSE); DESTINATION, TRANSIT Model 1: Attributes of alternatives COST, TIME Types of models Variables Figure 11-8: Methodological influence (NUMBER of Interview, DURATION of Interview); Influence of awareness This leads to the third group of model specifications summarised under “subjective motives” entering as alternative specific variables as well. Based on this hierarchy, three types of models are pursued with an additional test of the methodological influence for all of them (Figure 11-8). Awareness and Method Analysed types of models with selected variables in ascending order including methodological influence and influence of awareness across all models All three types of models are calculated for each scenario separately as well as conjointly resulting in each case in three binomial or multinomial logit models (Table 11-8). The data base is generated by all car driver trips collected in the SP survey. The kind of choice sample (original versus corrected mode choice), which is more promising, will be chosen due to a comparision of selected models, and allows conclusions on the quality of the SP answers. Additionally, those two samples will be merged and modelled with a dummy variable indicating the awareness of the respondents towards consistency of availability and choice of CLEVER. A possible methodical influence on mode choice will be examined and viewed critically. 127 Table 11-8: Setting of modelling Data set (scenarios) Choice sample Influence of awareness Car driver trips Based on the SP survey Scenario A → 3 (for each model type one) binomial logit models Scenario B → 3 binomial logit models (Scenario A,B) (→ 3 binomial logit models) Scenario C → 3 multinomial logit models Scenario A, B, C → 3 multinomial logit models Original (hypothetical) mode choice vs. corrected (realistic) mode choice. It has to be checked, which of those samples is more promising. Conclusions can be drawn on the quality of answers of the SP survey. Merge of the two samples to examine the influence of awareness. Sensitivity of method Influence of interviewer and duration of interview. A possible methodical influence on the mode choice will be examined. The following tables (Table 11-9 to Table 11-13) give an overview of the selected groups of variables according to their importance entering the model in descending order. Abbreviation, definition and scale of the variables are identified as provided in the model. Results of the correlation (dependent mode choice CLEVER) should ease the selection of variables primarily in respect of their significance and may indicate their performance in the model. Only variables with significance on the 5% level (α > 0.05, threshold for the rejection of the null hypothesis) have been selected. The attributes of alternatives “COST” and “TIME” as the hypothetical most important variables for mode choice have a very low correlation with the choice of CLEVER, but are highly significant (Table 11-9). The negative signs indicate that with rising costs and time the probability to choose CLEVER decreases. Due to these results it is significantly evident that these variables only play an inferior role in the decision process of mode choice. Table 11-9: Definition and results of correlation of selected variables of “Attributes of alternatives” Attributes of alternatives Matter of interest/ importance: Travel time and travel costs as objective attributes and decision criteria for mode choice (presented in the SP survey), important for assessment of scenarios. Variables Definition Scale COST Total travel costs in Euro. metric TIME Travel time in minutes. metric Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Mode Variable Mean CLEVER COST 2,20 4,27 1.065 -0,081 0,008 TIME 18,70 18,60 1.065 -0,113 0,000 COST 4,79 9,31 1.065 -0,081 0,008 TIME 22,37 20,93 1.065 -0,101 0,001 Car SD Cases 128 Correlation Significance The (low) negative but significant correlation of the three socio-demographic variables means that the probability to choose CLEVER decreases with increasing age and income and that male respondents tend to choose the new vehicle less often (Table 11-10). Considering trip related attributes (Table 11-11) yields that “LENGTH” and trip purpose “BUSSINESS” have a slight negative influence on the choice of CLEVER, while trip purpose “BRINGING” has a weak positive effect. It has to be noted that the dummy variables for trip purpose are mutually exclusive as they are treated similar to the alternative specific constants (compare chapter 11.3.4). Considering the variable trip length one constraint appears: as there is a perfect collinearity between trip length and travel costs (compare chapter 11.1.3) it is problematic to include both variables in the same model. The low negative correlation of the mobility pattern “DESTINATION” and “TRANSIT” indicates that with increasing number of destinations per egress (trip chaining) and with rising time in transit the probability to choose CLEVER decreases (Table 11-12). Table 11-10: Definition and results of correlation of selected variables of “Sociodemographic characteristics” Socio-demographic characteristics (all alternative specific – car) Matter of interest: Following socio-demographic variables are supposed to have a significant influence on mode choice. Variables Definition Scale M_MALE Dummy variable for gender with one for male and 0 for female respondents. dummy M_AGE Age in years. metric M_INC1 Household income in groups/categories in ascending order. ordinal Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Variable Mean GENDER M_MALE AGE INCOME SD Cases Correlation Significance 0,55 0,50 1.065 -0,115 0,000 M_AGE 49,74 13,34 1.053 -0,101 0,001 M_INC1 2,10 0,88 963 -0,306 0,000 129 Table 11-11: Definition and results of correlation of selected variables of “Trip related attributes” Trip related attributes (all alternative specific – car) Matter of interest: Trip length and trip purpose might be interesting to be examined. Variables Definition Scale M_KM Trip length in km. metric M_BUSI* Dummy variable for the trip purpose with one for “business trips”. dummy M_COMM* Dummy variable for the trip purpose with one for “commuter trips”. dummy M_EDU* Dummy variable for the trip purpose with one for “education trips”. dummy M_SHOP* Dummy variable for the trip purpose with one for “shopping trips”. dummy M_LEIS* Dummy variable for the trip purpose with one for “leisure trips”. dummy M_BRIN* Dummy variable for the trip purpose with one for “bringing & picking up dummy trips”. Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Variable Mean LENGTH M_KM BUSINESS SD Cases Correlation Significance 11,21 21,81 1.065 -0,081 0,008 M_BUSI 0,14 0,35 1.065 -0,166 0,000 COMMUTER M_COMM 0,25 0,43 1.065 -0,013 0,683** EDUCATION M_EDU 0,02 0,14 1.065 -0,009 0,780** SHOPPING M_SHOP 0,27 0,45 1.065 0,005 0,866** LEISURE M_LEIS 0,24 0,43 1.065 0,014 0,648** BRINGING M_BRIN 0,08 0,28 1.065 0,201 0,000 * The dummy variables for trip purpose are mutually exclusive. ** As the influence of the marked variables on the choice of CLEVER is not significant, they will not be considered in the model. Table 11-12: Definition and results of correlation of selected variables of “Mobility pattern” Mobility pattern (all alternative specific – car) Matter of interest: The following mobility patterns might be interesting to be entered into the model. Variables Definition Scale M_DES Number of destinations per egress (trip chaining). numerical M_TRIP Time in transit in minutes. metric Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Variable Mean SD Cases Correlation Significance DESTINATION M_DES 2,19 1,46 1.065 -0,151 0,000 TRANSIT M_TRIP 106,78 71,98 1.065 -0,160 0,000 130 The influence of subjetive attitudes as a result of the correlation can be described with a positive, significant effect of all five selected variables on the choice of CLEVER (Table 11-13). That means that the probability to choose CLEVER increases with positive associations regarding reasons for mode choice concerning costs and time and with a positive assessment of the CLEVER idea and design. Table 11-13: Definition and results of correlation of selected variables of “Subjective attitudes” Subjective attifudes (all alternative specific – car) Matter of interest: Subjective attitudes are important arguments in decision making. Variables Definition Scale M_RECOST Dummy variable for the reason of mode choice with one for “cost advantage”. dummy M_RETIME Dummy variable for the reason of mode choice with one for “time advantage”. dummy M_REENVIR Dummy variable for the reason of mode choice with one for “environmental considerations”. dummy M_CL_1 Dummy variable for the assessment of CLEVER with one for positive comments concerning “CLEVER Idea”. dummy M_CL_2 Dummy variable for the assessment of CLEVER with one for positive comments concerning “CLEVER Design”. dummy Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Variable Mean SD Cases Correlation Significance Reason COST M_RECOST 0,19 0,39 1.065 0,668 0,000 Reason TIME M_RETIME 0,05 0,21 1.065 0,391 0,000 Reason ENVIRONMENT M_REENVIR 0,07 0,25 1.065 0,614 0,000 CLEVER Idea M_CL_1 2,70 0,99 1.065 0,300 0,000 CLEVER Design M_CL_2 2,38 0,99 1.065 0,379 0,000 The correlations indicate the bivariat relationship between the dependent (choice of CLEVER) variable and each of the independent variables, ignoring possible interactions between independent variables. They serve as a mean of exploring relevant predictors (significant variables) for the subsequent step of modelling. 11.3.3 Methodological influence The survey method (SP interview and its attributes e.g. interview duration) and the interviewer him/herself should actually have no influence on the SP mode choice of the respondents. But in fact it can not be rejected. In Graz 73 SP household interviews (with 98 persons having 355 car driver trips) were done by nine interviewers, with one of them did more than half of all interviews (51,4%; 37), another made 14 (19,4%) and the others 131 between one and five interviews (1,4% – 6,9%) (Figure 11-9). The figure on the right side shows this variable prepared to enter the model [Interview number in ascending order] (for explanation: 42,5% of the interviews have been the 5th interview (actually from the first to the fifth interview) of an interviewer, whereas 8,2% have been the 20th interview and so on). It is supposed that more experience and practice in interviewing could either have a positive or a negative effect – both could be possible – on the choice of CLEVER. The same applies to a possible influence of the duration of an interview. The average duration of one interview has been 68 minutes. The majority of the interviews has lasted between 60 (23,3%) and 90 minutes (24,7%) (Figure 11-10). 50% 1,4% n = 73 interviews 2,8% 40% 4,2% 4,2% 42,5% 4,2% 30% 5,6% Interviewer 1 51,4% 6,9% 20% y = -0,1655Ln(x) + 0,3444 R2 = 0,7869 Interviewer 2 10% 13,7% 13,7% 19,4% n = 73 SP interviews n interviewers = 9 8,2% 6,8% 6,8% 25th 30th 8,2% 0% 5th Percentage of SP interviews per interviewer 10th 15th 20th > 30th Number of SP interview done by the interviewers Figure 11-9: Percentage and number of SP interviews done by the interviewers 30% n = 73 interviews 25% m = 68,4 min 24,7% 23,3% 20% 15% 12,3% 10% 11,0% 9,6% 8,2% 5% 1,4% 4,1% 2,7% 2,7% 0% 30 min 40 min 50 min 60 min 70 min 80 min 90 min 100 min 110 min 120 min Duration of SP interviews in minutes Figure 11-10: Distribution of duration of SP interviews These two discussed variables “Interview number” and “Interview duration” enter the model to test the methodological influence. The results of the correlation show that there is a significant negative correlation between the choice of CLEVER and the two variables (Table 11-14). In both cases this negative correlation means that the probability to choose CLEVER decreases with an increasing duration of the interview and with an increasing number of interviews. To find an explanation one can assume that the interest for CLEVER declined at 132 both sides (respondents’ and interviewers’) with rising interview duration and with interviewers’ greater routine influencing the choice of CLEVER in a negative way. Table 11-14: Definition and results of correlation of selected variables for testing the methodological influence Methodological influence Matter of interest: The methodical influence will be tested across all the 3 types of models. Variables Definition Scale INTERVIEW NUMBER Number of interview an interviewer has done in ascending order (alternative specific – car). metric INTERVIEW metric Duration of the interview in minutes (alternative specific – car). Results of correlation (sample: car driver trips; mode choice: original; scenarios: ABC, unweighted) Dependent Variable: Mode Choice CLEVER Variable Mean SD Cases Correlation Significance INTERVIEW NUMBER M_INT_NO 11,79 10,21 1.065 -0,294 0,000 INTERVIEW M_INTMIN 72,63 22,05 1.065 -0,239 0,000 11.3.4 Editing of variables for modelling For modelling the data, the statistical software package “Limdep 8.0/Nlogit 3.0” developed by Econometric Software, Inc. was used. “Limdep (LIMited DEPendent variable models) is an integrated program for estimation and analysis of linear and nonlinear models. NLOGIT Version 3.0 is an extension of LIMDEP that, in addition to all features of LIMDEP, provides programs for estimation, model simulation and analysis of multinomial choice data, such as brand choice, transportation mode, and all manner of survey and market data in which consumers choose among a set of competing alternatives. NLOGIT has become the standard package for estimation and simulation of multinomial discrete choice models (www.limdep.com).” For operating NLOGIT, data have to be edited in an Excel sheet, where the rows represent the alternatives (e. g. transport modes) and columns represent the variables. As the model is based on the trip level, data have to be structured according to the hierarchy “household – person – trip” shown in Table 11-15. Two alternatives (in a binomial model) are available at least for each trip, defined in the column “alternative” and specified in the column “mode”. The “number of alternatives” can be selected case specifically in multinomial models – which applies to modelling the data of Scenario C. “Mode choice”, which is the dependent variable, is entered with 1 for the chosen alternative and 0 for the other one. 133 Table 11-15: Hierarchy and structure of the edited data Household ID Person ID Trip ID Alternative Mode Number of alternatives Mode choice 1000 100001 10000101 Car driver 1 2 1 … … … CLEVER 2 2 0 … … 10000102 Car driver 1 2 1 … … … CLEVER 2 2 0 … … 10000103 Car driver 1 2 0 … … … CLEVER 2 2 1 … … 10000104 Car driver 1 2 0 … … … CLEVER 2 2 1 1000 100002 10000201 Car driver 1 2 1 … … … CLEVER 2 2 0 … … 10000202 Car driver 1 2 1 … … … CLEVER 2 2 0 … … … … … … … As a basic principle, variables are entered into the model either as a numerical or a dummy (0 or 1) variable. Three types of explaining variables can be distinguished [MAIER G., WEISS P. (1990)]: – ASC – alternative specific constant: One explaining variable takes the value “1“ for one alternative, while the value of all the other alternatives is “0“. Alternative specific constants can be generated for I – 1 alternatives at most, when I is indicating the number of alternatives (Table 11-16). The alternative specific constant expresses the differences of utilities of the single modes, in case all other attributes are identical. They are in a close relationship with the observed relative frequencies. Table 11-16: – Alternative specific constants for five alternative modes Trip ID Alternative Mode ASC_car driver ASC_CLEVER ASC_car passenger ASC_PT 10000101 Car driver 1 1 0 0 0 … CLEVER 2 0 1 0 0 Car passenger 3 0 0 1 0 PT 4 0 0 0 1 Bicycle 5 0 0 0 0 Generic variables are explaining variables that vary across the alternatives. They arise from the attributes of alternatives. In most of the cases they vary across the individuals at the same time, tough the figures can be identical for all individuals as well. Generic variables are for example “travel time” and “travel costs” (Table 11-17), which are mode specific and provide the basis for mode choice. 134 Table 11-17: – Example for generic variables „travel time“ and „travel costs“ Trip ID Alternative Mode Travel time [min.] Travel costs [€] 10000101 Car driver 1 15 0,40 … CLEVER 2 10 0,20 Car passenger 3 15 0,00 PT 4 40 1,70 Bicycle 5 60 0,00 Alternative specific (socio-economic) variables are similarily structured as the alternative specific constants, though they vary across the individuals. They arise from the socio-economic attributes of the decision makers. They take the value of the socioeconomic attribute of the individual allocated to one (arbitrarily chosen) alternative; all the others take the value “0”. This kind of specification expresses the hypothesis that the assessment of the alternative “car driver” varies with the specific attribute (e.g. age or gender) (Table 11-18). Table 11-18: Example for the alternative specific socio-economic variables “age” and gender” Trip ID Alternative Mode Age [years] Gender [1 = male] 10000101 Car driver 1 40 1 … CLEVER 2 0 0 Car passenger 3 0 0 PT 4 0 0 Bicycle 5 0 0 „A fundamental property of logit models is that only differences in representative utility affect the choice probabilities, not their absolute levels. … If the researcher believes that a factor that does not vary over alternatives (e.g. any characteristics of the decisionmaker) affects the decisionmaker’s choices, then it must be entered into representative utility in a meaningful fashion. In particular, it must interact with a variable that varies over alternatives [TRAIN K. (1986)]” The Limdep command for a discrete choice model with for example, the two alternatives car and CLEVER, the generic variables COST and TIME and the alternative specific constant ONE is the following: --> NLOGIT;Lhs=CHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME;Wts=P_WEIGHT; Crosstab;Describe$ 135 11.4 Discrete Choice Model and Estimation 11.4.1 Approach of modelling Exploring influencing variables on the choice of car or the environmentally friendly CLEVER, a set of logit models has been conducted. Starting with the model of constants and the model of attributes of alternatives, variations are tested in the following, entering selected variables according to chapter 11.3.2. Whether the additional variables add explanatory power to the model or not is tested by the likelihood ratio test, in which the log likelihood functions of the unrestricted and restricted models of interest are compared [BEN-AKIVA M., LERMAN S.R. (1985)]: “We will let LLu and LLR denote the values of the log likelihood functions at its maximum for the unrestricted and restricted models, respectively. Again let r denote the number of independent restrictions imposed on the parameters in computing LLR. One can show that LLu ≥ LLR. The test statistic for the null hypothesis that the restrictions are true is − 2 * ( LLR − LLu ) , which is asymptotically distributed as Χ² (chi squared) with r degrees of freedom. Thus if –2*(LLR – LLu) is “large” in the statistical sense, we reject the null hypothesis that the restrictions are true.” The likelihood ratio test is as well applied comparing the results of the models based on the original and the corrected choice sample. The variations of the results of the two samples are viewed critically with regard to the quality of answers of the SP survey. The focus of modelling is on the one hand on the attributes of alternatives (representing the scenarios) and their role in choosing CLEVER or car and on the other hand on finding a model with the highest explanatory power. The influence of awareness of the respondents on the mode choice itself as well as the methodological influence on the results are of special interest and will be examined separately. 11.4.2 Attributes of alternatives Attributes of alternatives describe the alternatives car and CLEVER and vary over the three scenarios. They have been provided as objective decision criteria for mode choice in the SP survey and enter the logit model as generic variables (Table 11-19). Table 11-19: Definition of the variables “Attributes of alternatives” Attributes of alternatives Variables Definition Scale COST Total travel costs in Euro. metric TIME Travel time in minutes. metric 136 The probability that an individual will choose alternative car or alternative CLEVER is estimated according to the binomial logit model: Pcar = e e V( car ) V( car ) +e V( CLEVER ) PCLEVER = or equivalent V( car ) = β car + β COST * COSTcar + β TIME * TIMEcar e e V( CLEVER ) V( CLEVER ) +e with V( car ) and V(CLEVER ) = β COST * COSTCLEVER + β TIME * TIMECLEVER where V( car ) and V( CLEVER ) represent the attractiveness of the respective alternative. Beside the generic variables COST and TIME, which vary across the alternatives as well as across the scenarios, an alternative specific constant (ASC) for car (A_CAR) has been added. It reflects the difference of utilities of the alternatives in case all other characteristics are equal. Alternative specific constants (ASC) are a special characteristic of discrete choice models [MAIER G., WEISS P. (1990)]. They are dummy variables, reflecting the unspecific utility of an alternative and interpreted by HULTKRANTZ L. and MORTAZAVI R. (1998) as a “cognitive threshold”. A maximum of I –1 alternative specific constants may be included in the model, in which I is the maximum number of alternatives (I = maxnIn). A model of constants reflects the situation, when all other explanatory variables are zero. It actually demonstrates the observed relative frequencies without any explanatory content. Its quality arises from a comparison with models comprising explanatory variables. An increase of the log likelihood of the more elaborate model compared to the model of constants justifies the entry of additional variables. In general, it can be assumed that all unobserved or unmeasured issues are implied in the ASC. In this regard, the model “Attributes of alternatives” is compared with the “Model of constants”. Table 11-20 lists the variables included in the two types of models. Table 11-20: Variables included in the “Model of constants” and in “Attributes of alternatives” according to scenarios Variables included in the “Model of constants” and “Attributes of alternatives” Model / Scenario A B C AB ABC Model of constants (including only ASC alternative specific constants) A_CAR A_CAR A_CAR A_CAR A_CAR Attributes of alternatives COST COST COST COST COST A_CAR TIME TIME TIME TIME A_CAR A_CAR A_CAR A_CAR 137 A_CLEVER A_CLEVER A_PT A_PT A_CLEVER A_CLEVER A_PT A_PT Comparing the likelihood of the model of constants with the one of the model including attributes of alternatives (Table 11-21) by means of the likelihood ratio test yields that there is no significant difference of the two types of models in scenario A (as well as for scenario AB), neither comparing the likelihood of the original choice sample (χ² of 0,0344 with 1 degree of freedom = 0,85285855) nor the one of the reduced choice sample (χ² of 0,3642 with 1 degree of freedom = 0,546182865) (Table 11-22). That means that the additional variable COST in scenario A (and in scenario AB) does not have an influence on the attractiveness of the alternatives and does not improve the model. A significant improvement of the model of constants adding the variables COST and TIME can be distinguished for scenario B (only for the reduced choice sample: χ² of 10,77378 with 2 degree of freedom = 0,00457618), as well as for scenario C and ABC (original and corrected sample). Table 11-21: Comparison of the results of the logit models “Model of constants” and “Attributes of alternatives” according to scenarios and choice samples Attributes of alternatives (COST and TIME) Scenario A B C AB ABC Model Binomial Logit Binomial Logit MNL Binomial Logit MNL Number of observations 355 355 354 710 1064 Log lik. -143.4805 -151.5781 -176.3787 -295.2001 -472.6088 Adjusted R² .36318 .32534 .60707 .34397 .64968 Log lik. -59.26421 -69.46357 -85.12873 -128.9222 -214.2685 Adjusted R² .73622 .69082 .81036 .71349 .84118 355 355 354 710 1064 Log lik. -143.4633 -149.0380 -170.4278 -293.2732 -467.4424 Adjusted R² .35965 .33288 .61957 .34641 .65311 Log lik. -59.08211 -64.07668 -80.49319 -124.7472 -205.4809 Adjusted R² .73629 .71318 .82032 .72199 .84751 Model of constants Original choice Corrected choice Model with attributes of alternatives Number of observations Table 11-22: Original choice Corrected choice Results of the likelihood ratio χ² tests comparing logit model of constants with model including attributes of alternatives (corrected choice) according to the scenarios Likelihood ratio χ² test: Attributes of alternatives Scenario A B C AB ABC Model Binomial Logit Binomial Logit MNL Binomial Logit MNL u Model of constants vs. Attributes of alternatives original choice Significance χ² u corrected choice Significance χ² o -2*(LL – LL ) Significance χ² u Attributes of alternatives: original vs. corrected choice o -2*(LL – LL ) u Model of constants: original vs. corrected choice o -2*(LL – LL ) o -2*(LL – LL ) Significance χ² 0,0344 5,0802 11,9018 3,8538 10,3328 0,852859 0,078859 0,002604 0,145599 0,005705 0,3642 10,77378 9,27108 8,35 17,5752 0,546183 0,004576 0,009701 0,015375 0,000153 168,43258 164,22906 182,49994 332,5558 516,6806 1,62748E-38 1,3481E-37 1,3791E-41 2,6677E-74 2,232E-114 168,76238 169,92264 179,86922 337,052 523,923 1,37873E-38 7,6924E-39 5,1758E-41 2,7983E-75 5,93E116 138 The distinction between original and corrected choice samples leads to a significant improvement of the likelihood as well as of the adjusted R² for all corrected choices of all scenarios and for both types of models (model of constants and model including attributes of alternatives). That reveals that the results of modelling corrected choices – which implies reconsidering and revising the primary SP choice – are more probable than those of the original choices, which justifies the step of correction. The probability to choose CLEVER generally decreases just as well as the margin to identify variables with explanatory power for the choice and use of CLEVER, which means that there are a lot of unobserved influences implied in the ASC. Explaining the parameters of the choice model with attributes of alternatives according to scenarios and choice samples confirms for Scenario A that the variable COST is neither significant nor has its coefficient the expected negative sign (Table 11-23). The probability to choose car is for both samples (original and corrected) a priori very high, indicated by the coefficient of the ASC (A_CAR), which is highly significant and many times higher than the one of the variable COST. The additional variable COST has no significant influence on the SP mode choice. Table 11-23: Results of the discrete choice estimation (weighted) for scenario A with attribute of alternatives COST including ASC for original and corrected mode choice Choice sample Variable A original mode choice COST A corrected mode choice COST A_CAR* A_CAR* Coefficient Standard Erorr t-value Significance Log likelihood .00693072 .03807733 .182 .8556 1.63497199 .17240141 9.484 .0000 .05173634 .09871622 .524 .6002 2.96301969 .31774511 9.325 .0000 Adjusted R² -143.4633 .35965 -59.08211 .73629 * A_CAR … Alternative specific constant (ASC) for car The question arises how the results of the models change excluding the alternative specific constant. Table 11-24 gives the answer: the adjusted R² as well as the log likelihood decreases in both samples (original and corrected mode choice) for Scenario A. As an increase of the log likelihood of the more elaborate model justifies the entry of additional variables [MAIER G., WEISS P. (1990)] and the unobserved influence on mode choice must not be ignored, this approach is generally not being followed for the subsequent models, but it is shown as an interesting comparison in this chapter for the other models including “Attributes of Alternatives”. Table 11-24: Results of the discrete choice estimation (weighted) for scenario A with attribute of alternatives COST without ASC for original and corrected mode choice Choice sample Variable Coefficient Standard Erorr t-value A original COST .43931176 .07954967 5.522 .0000 -192.8920 .14145 A corrected COST 9.95701705 .02426163 410.402 .0000 -149.2309 .33579 139 Significance Log likelihood Adjusted R² The calculation of the mode choice probability according to the results of the discrete choice estimation for Scenario A based on the SP answers shows that the probability to choose car is in case of the original sample above 83% and for the corrected choice above 95% with a slight increase with rising car costs (Figure 11-11: left: calculated according to the data of the SP survey; right: abstract differences between car and CLEVER costs with intervals of € 0,10). The complementary CLEVER probabilities lie between 17% (original) and 5% (corrected), decreasing with rising car costs. 1 1 0,9 0,9 0,8 0,8 Probability of mode choice Probability of mode choice The hypothesis that cost advantages for CLEVER lead to an increasing choice of CLEVER could not be verified for scenario A. This could be on the one hand explained by the fact that rising car costs implicate a rising trip length, which is a limiting factor for the use of CLEVER (limited range), on the other hand it has to be considered that the variance of car and CLEVER costs is extremly small and has maybe been disregarded by the respondents. However, the variable COST is neither significant for mode choice in scenario A nor has its coefficient the expected negative sign. 0,7 P_car_original choice 0,6 P_CLEVER_original choice P_car_corrected choice 0,5 P_CLEVER_corrected choice 0,4 0,3 0,7 P_car_original choice 0,6 P_car_corrected choice P_CLEVER_corrected choice 0,4 0,3 0,2 0,2 0,1 0,1 0 P_CLEVER_original choice 0,5 0 0 10 20 30 40 50 Travel costs (car) in Scenario A [€] 60 70 80 n = 355 0 5 10 15 20 Cost advantage for CLEVER in Scenario A [€] 25 30 n = 301 Figure 11-11: Probabilities of mode choice in Scenario A dependent on travel costs (car) (left figure) and cost advantage for CLEVER (right figure) The results of scenario B concerning the variable COST are similar to those of scenario A: the COST variable is again neither significant nor has its coefficient the expected negative sign (Table 11-25). In scenario B variable TIME is entering the model. It has a significant influence on mode choice and its coefficient has the expected negative sign. Nevertheless, the alternative specific constant (A_CAR) exceeds the effect of the entering variables on a large scale, reflecting the high degree of unobserved influences. Generally it can be said that again the model, which is based on the corrected mode choice, leads to an improved model regarding the likelihood and the adjusted R² (Table 11-21 and Table 11-22). Modelling scenario B without the alternative specific constant (Table 11-26) results in a notable decrease of the adjusted R² and of the log likelihood (especially at the corrected sample), though both variables COST and TIME are significant. 140 Table 11-25: Coefficient Standard Erorr t-value Significance Log likelihood Scenario B original mode choice Variable COST .07555944 .05157408 1.465 .1429 TIME -.06605730 .03008935 -2.195 .0281 A_CAR* 1.72262688 .19766832 8.715 .0000 B corrected mode choice Choice sample Results of the discrete choice estimation (weighted) for Scenario B with attributes of alternatives COST and TIME for original and corrected mode choice COST .38192747 .19040439 2.006 .0449 TIME -.16041733 .05240815 -3.061 .0022 A_CAR* 3.10064393 .33566732 9.237 .0000 Adjusted R² -149.0380 .33288 -64.07668 .71318 * A_CAR … Alternative specific constant (ASC) for car Table 11-26: Choice sample Results of the discrete choice estimation (weighted) for Scenario B with attributes of alternatives COST and TIME for without ASC original and corrected mode choice Variable COST B original mode choice TIME B corrected COST mode choice TIME Coefficient Standard Erorr t-value Significance Log likelihood .18848097 .08151621 2.312 .0208 -.08775847 .03337128 2.630 .0085 7.15418959 .03694241 193.658 .0000 -.43062231 .03058042 -14.082 .0000 Adjusted R² -192.0857 .14262 -172.2136 .23132 The coefficients of the discrete choice model can not be directly interpreted [MAIER G., WEISS P. (1990)], but the ratio of the coefficients may be estimated. The tradeoff ratio implied by the coefficients TIME (βTIME) and COST (βCOST) yields the value of time, which is a “key concept in transport planning in terms of economic valuation of travel time savings and the relative importance of time versus cost in travel forecasting models [BEN-AKIVA M., BOLDUC D., BRADLEY M. (1993).” However, the estimation of the value of time for the model “Attributes of alternatives” (including ASC) for Scenario B predicts a negative value (0.87 min/€ for the original choice and - 0.42 min/€ for the corrected one), which results from the positive (against expectations) COST coefficient. As the variable COST is not significant, the value of time can not be interpreted plausibly. According to these considerations the probabilities to choose car increase with rising car costs while the probability to choose CLEVER decreases (Figure 11-12 left and Figure 11-15 left), which is against expectations but corresponds with the positive COST coefficient. Car probabilities according to the variable COST (which have been calculated according to the SP data entering the variable TIME as mean value) vary between 80% to 99% for the original and 90% to 99% for the corrected choice with the complementary CLEVER choices. 141 1 0,9 0,8 0,8 Probability of mode choice Probability of mode choice 1 0,9 0,7 0,6 P_car_original choice 0,5 P_CLEVER_original choice P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,2 0,1 0,7 0,6 P_car_original choice 0,5 P_CLEVER_original choice P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,2 0,1 0 0 0 10 20 30 40 50 Travel costs (car) in Scenario B [€] 60 70 80 0 10 n = 355 car driver trips 20 30 40 50 Travel time (car) in Scenario B [min] 60 70 80 n = 355 car driver trips Figure 11-12: Probabilities of mode choice in Scenario B dependent on travel costs (car) (left figure) and travel time (right figure) 1 1 0,9 0,9 0,8 0,8 Probability of mode choice Probability of mode choice Having a look at the probabilities of car and CLEVER choice according to travel time (variable COST enters the calculation as mean value) (Figure 11-12 right), it is noticeable that all four graphs split at the point of 15 to 20 minutes. This distinctive feature can be traced back to the assumptions of the SP survey, where the coding of travel time for CLEVER distinguished two respectively four interview groups (compare chapter 7.2.4). The calculations of travel time for CLEVER are based on the assumption of 10% time savings for CLEVER compared to a car trip in interview group 1 and 3 and 40% time savings in interview group 2 and 4. Separating the data and probabilities according to these interview groups results in more reasonable graphs (Figure 11-13). In both settings probability to choose CLEVER increases with growing (car) travel time, complementary car choice decreases. While the group with 10% time savings vary between 2% and 5% CLEVER choice (reduced sample), the differences with 40% time savings are more explicit and rise up to 45% CLEVER choice. 0,7 P_car_original choice 0,6 P_CLEVER_original choice P_car_corrected choice 0,5 P_CLEVER_corrected choice 0,4 0,3 0,7 0,6 P_car_original choice 0,5 P_CLEVER_original choice P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,2 0,2 0,1 0,1 0 0 0 0 10 20 30 40 50 Travel time (car) in Scenario B [min] Interview group 1 and 3 60 70 80 n = 169 car driver trips 10 20 30 40 50 Travel time (car) in Scenario B [min] Interview group 2 and 4 60 70 80 n = 186 car driver trips Figure 11-13: Probabilities of mode choice in Scenario B dependent on travel time (car) distinguished according to interview groups Illustrating mode choice probabilities in Scenario B on the basis of the differences between travel time CLEVER and car results in Figure 11-14. It can be seen that a considerable 142 increase of CLEVER choice proceeds in both groups from the point of 10 minutes time advantage for CLEVER compared to car. 1 1 0,9 0,9 0,8 0,8 Probability of mode choice Probability of mode choice n = 186 car driver trips 0,7 P_car_original choice 0,6 P_CLEVER_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,4 n = 169 car driver trips 0,3 0,2 0,7 0,6 P_car_original choice P_CLEVER_original choice 0,5 P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,2 0,1 0,1 0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 -30 0 -25 -20 -15 -10 -5 0 Difference concerning travel time CLEVER - car in Scenario B [min] Interview group 2 and 4 Difference concerning trip time CLEVER - car in Scenario B [min] Interview group 1 and 3 Figure 11-14: Probabilities of mode choice in Scenario B according to time savings for CLEVER (difference concerning travel time CLEVER – travel time car) distinguished according to interview groups An abstract application of the logit model for cost (with intervals of 0,1 €) and time advantage (with intervals of 1 min) for CLEVER produces Figure 11-15. Cost advantages have no significant influence on the choice of CLEVER leading to a decreasing choice probability, which could be explained by the lack of orthogonality and the resulting correlation of costs and time. Time savings result in the typical graph for logit models (corrected choice sample); with rising time advantage the probability to choose CLEVER increases, while the probability to choose the car decreases. 1 1 0,9 0,8 0,8 Probability of mode choice Probability of mode choice 0,9 0,7 0,6 P_car_original choice 0,5 P_CLEVER_original choice P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,7 0,6 P_car_original choice P_CLEVER_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,4 0,3 0,2 0,2 0,1 0,1 0 0 0 5 10 15 20 Cost advantage for CLEVER in Scenario B [min] 25 0 30 n = 301 10 20 30 40 50 Time advantage for CLEVER in Scenario B [min] 60 n = 61 Figure 11-15: Probabilities of mode choice in Scenario B according to cost advanatges (left) and time advantages (right) for CLEVER For Scenario C a multinomial logit model has been calculated as, beside car and CLEVER, the alternatives public transport and bicycle have been added. The availability of the additional modes for the respondents as well as the availability of data (e.g. trip costs and trip time for PT) has been considered, resulting in a variable number of alternatives per respondent. Again a comparison of the log likelihood and the adjusted R² shows that the 143 model based on the corrected choice sample is more probable than the other one, which is as well confirmed by the likelihood ratio test (Table 11-21 and Table 11-22). The parameters of the estimation reveal the significance of the generic variable TIME and the alternative specific constant (ASC) for car and CLEVER – indicating the unobserved attributes of the modes as well as of the respondents – while COST and ASC for public transport are not significant (Table 11-27). For comparision Table 11-28 shows the results of the discrete choice estimation of Scenario C without the alternative specific constants, again with wide difference of the log likelihood and the adjusted R² compared to the model including the ASC. Table 11-27: Scenario C corrected mode choice Scenario C original mode choice Choice sample Results of the discrete choice estimation (weighted) for Scenario C including attributes of alternatives COST and TIME for original and corrected mode choice Variable Coefficient Standard Erorr t-value Significance Log likelihood COST .19337365 .07573224 2.553 .0107 TIME -.05026118 .02908119 -1.728 .0839 A_CAR* 5.23492323 1.20413568 4.347 .0000 A_CLEVER** 4.08796941 1.20348659 3.397 .0007 A_PT*** .21673899 1.55374740 .139 .8891 COST .21810419 .11352774 1.921 .0547 TIME -.09229147 .03325675 -2.775 .0055 A_CAR* 5.78269901 1.27232203 4.545 .0000 A_CLEVER** 3.03705597 1.28674579 2.360 .0183 .06363630 1.61875999 .039 .9686 A_PT*** Adjusted R² -170.4278 .61957 -80.49319 .82032 * A_CAR ** A_CLEVER *** A_PT Alternative specific constant (ASC) for car ASC for CLEVER ASC for public transport (PT) Table 11-28: Results of the discrete choice estimation (weighted) for Scenario C with attributes of alternatives COST and TIME for without ASC original and corrected mode choice Choice sample Variable COST C original mode choice TIME C corrected COST mode choice TIME Coefficient Standard Erorr t-value Significance Log likelihood .70980314 .07186198 9.877 .0000 -.06797115 .01265172 -5.372 .0000 3.8477 .02060392 186.747 .0000 -.18891362 .00994694 -18.992 .0000 Adjusted R² -295.2638 .34289 -394.2569 .12258 Calculating the probabilities according to the MNL, variables COST and TIME of the additional alternatives PT and bicycle entered the equation as mean values. As the probabilities to choose either PT or bicycle are evanescent (< 0,1%) they have not been added to the following figures. The results of Scenario C are similar to those of scenario B. The variable COST does not add any explanatory power to the model, while travel time has a significant influence on the choice of car or CLEVER (Figure 11-16). Again a distinction according to the interview groups has to be made when calculating the probabilities dependent on the variable TIME in order to get reasonable graphs (Figure 11-17). 144 1 1 0,9 0,9 0,8 Probability of mode choice Probability of mode choice 0,8 0,7 P_car_original choice 0,6 P_CLEVER_original choice P_car_corrected choice 0,5 P_CLEVER_corrected choice 0,4 0,3 0,7 0,6 P_car_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,3 0,2 0,2 0,1 0,1 0 P_CLEVER_original choice 0,4 0 0 10 20 30 40 50 60 Travel costs (car) in Scenario C [€] 70 80 0 10 20 n = 355 car driver trips 30 40 50 60 Travel time (car) in Scenario C [min] 70 80 n = 355 car driver trips Figure 11-16: Probabilities of mode choice in Scenario C dependent on travel costs (car) (left figure) and travel time (right figure) 1 1 0,9 0,9 0,8 0,8 Probability of mode choice Probability of mode choice The hypothesis, that with rising (car) travel time (which implicates a time advantage using CLEVER) the probability to choose CLEVER increases dependent on the extent of time advantage (10% on the left and 40% on the right Figure 11-17), is also valid for Scenario C. 0,7 P_car_original choice 0,6 P_CLEVER_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,4 n = 169 car driver trips 0,3 0,2 0,1 0,7 0,6 P_car_original choice P_CLEVER_original choice 0,5 P_car_corrected choice 0,4 P_CLEVER_corrected choice 0,3 0,2 0,1 0 0 0 10 20 30 40 50 60 70 80 Travel time (car) in Scenario C [min] Interview group 1 and 3 0 10 20 30 40 Travel time (car) in Scenario C [min] Interview group 2 and 4 50 60 n = 186 car driver trips Figure 11-17: Probabilities of mode choice in Scenario C dependent on travel time distinguished according to interview groups Figure 11-18 emphasises this result, e.g. a 10 minutes time advantage for CLEVER results in a choice probability of 5% (corrected choice) in the sample with 10% time savings for CLEVER compared to car (left figure) and 8%, when the time gain for CLEVER is 40% (right figure). 145 1 0,9 0,8 0,8 Probability of mode choice 1 0,9 Probability of mode choice 0,7 P_car_original choice 0,6 P_CLEVER_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,4 n = 169 car driver trips 0,3 0,2 0,1 0,7 P_car_original choice 0,6 P_CLEVER_original choice 0,5 P_car_corrected choice P_CLEVER_corrected choice 0,4 n = 169 car driver trips 0,3 0,2 0,1 0 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 -20 0 Difference concerning travel time CLEVER - car in Scenario C [min] Interview group 1 and 3 Figure 11-18: -18 -16 -14 -12 -10 -8 -6 -4 -2 0 Difference concerning travel time CLEVER - car in Scenario C [min] Interview group 2 and 4 Probabilities of mode choice in Scenario C according to time savings for CLEVER (difference concerning travel time CLEVER – travel time car) distinguished according to interview groups The abstract view on cost (calculated with an interval of 0,1 €) and time advantage (with an interval of 1 min) for CLEVER in scenario C summarises the results above. While cost advantages have no influence on the choice of CLEVER, the significant attribute of alternative TIME has a positive effect on it (Figure 11-19). The probability to choose CLEVER increases with rising time advantage compared to car, which results in the typical logit curve, complementary the probability to choose car decreases (Figure 11-19 right side). 1 1 0,9 0,9 0,8 0,8 P_car_original choice P_CLEVER_original choice Probability of mode choice Probability of mode choice P_car_corrected choice 0,7 P_car_original choice 0,6 P_CLEVER_original choice P_car_corrected choice 0,5 P_CLEVER_corrected choice 0,4 0,3 P_CLEVER_corrected choice 0,7 0,6 0,5 0,4 0,3 0,2 0,2 0,1 0,1 0 0 0 5 10 15 20 Cost advantage for CLEVER in Scenario C [min] 25 0 30 n = 301 10 20 30 40 50 Time advantage for CLEVER in Scenario C [min] 60 n = 61 Figure 11-19: Probabilities of mode choice in Scenario C according to cost advantage (left) and time advantage (right) for CLEVER After modelling the three scenarios A, B and C separately in order to examine the explicit scenario effect, they have been merged into one model. The resulting parameters can be extracted out of Table 11-29. Due to the rising number of observations the log likelihood increases, while the other parameters do not differ from the results of the single models: Modelling the corrected mode choice reveals more probable results than the original sample, the variable COST is neither significant nor has its coefficient the expected negative sign, while the variable TIME is significant with a negative coefficient. The explanatory value of the alternative specific constants for car and CLEVER is once again significant and reflects the unobserved attributes and influences. 146 Table 11-29: Scenario A, B, C corrected mode choice Scenario A, B, C original mode choice Choice sample * Results of the discrete choice estimation (weighted) for A,B,C including attributes of alternatives COST and TIME for original and corrected mode choice Variable Coefficient Standard Erorr t-value Significance Log likelihood COST .07386108 .02975085 2.483 .0130 TIME -.04128280 .01544455 -2.673 .0075 A_CAR* 5.46012503 1.19880525 4.555 .0000 A_CLEVER** 3.97923266 1.20029464 3.315 .0009 A_PT*** .22114867 1.56067319 .142 .8873 COST .19664582 .07810953 2.518 .0118 TIME -.09212861 .02372650 -3.883 .0001 A_CAR* 5.81488054 1.24504445 4.670 .0000 A_CLEVER** 2.97091524 1.25069426 2.375 .0175 A_PT*** -.09792322 1.65153206 -.059 .9527 Adjusted R² -467.4424 .65311 -205.4809 .84751 A_CAR Alternative specific constant (ASC) for car; ** A_CLEVER ASC for CLEVER; *** A_PT ASC for PT Functions of the attributes of alternatives trip costs and trip time (cost², costln, time², timeln) have been modelled as well but have not led to an improvement of the models. The influence of the variable fuel costs has also been tested, but gives approximately equivalent results like modelling the total costs; therefore the variable COST has been kept subsequently. 11.4.3 Socio-demographic characteristics Socio-demographic characteristics enter the logit model as alternative-specific socioeconomic variables. They describe the respondents and comprise as preselected gender, age and income. In addition, mobility pattern as well as trip related attributes are considered to roughly characterise the mobility behaviour of the respondents and their trips (Table 11-30). This group of variables does not vary across the scenarios but stay the same for all of them. Table 11-30: Definition of the variables socio-demographic characteristics, trip related attributes and mobility pattern Socio-demographic characteristics (all alternative specific – car) Variables Definition Scale GENDER M_MALE Dummy variable for gender with one for male respondents. dummy AGE M_AGE Age in years. metric INCOME M_INC1 Household income in groups/categories in ascending order. ordinal Mobility pattern (all alternative specific – car) DESTINATION M_DES Number of destinations per egress (trip chaining). numerical TRANSIT M_TRIP Time in transit in minutes. metric Trip related attributes (all alternative specific – car) (LENGTH) (M_KM) (Trip length in km.) (metric) BUSINESS M_BUSI Trip purpose with one for “business trips”. dummy BRINGING M_BRIN Trip purpose with one for “bringing & picking up trips”. dummy 147 As all three groups of variables are alternative specific they had to be allocated to one alternative. As these variables characterise the original mode choice (of the RP survey) the alternative car has been selected, which results in the following utility specifications for car and CLEVER for the binomial logit model examplary including socio-demographic characteristics: V( car ) = β car + β COST * COSTcar + β TIME * TIMEcar + β GENDER * GENDERcar + β AGE * AGEcar + β INCOME * INCOMEcar V( CLEVER ) = β COST * COSTCLEVER + β TIME * TIMECLEVER A comparison of the results of the logit models looking at the log likelihood and the adjusted R² (Table 11-31) and according to likelihood ratio χ² tests (Table 11-32) shows that the selected socio-demographic characteristics add explanatory power to the original mode choice model including only attributes of alternatives. A significant improvement of the log likelihood justifies the addition of those three variables. In a second step, two variables summarised as “mobility pattern” enter the model, again enhancing the model significantly, quantified in improved log likelihood and adjusted R². Entering trip related attributes (trip length, reason “business” and reason “bringing and picking up”) in a third step raises the question about the need and plausibility of those variables. As it has been proven in chapter 11.1.3 that trip length and travel costs are perfectly collinear, it is evident that trip length can be omitted in a model when travel costs are included (estimating the models with and without trip length supported this assumption). Concerning the variables describing two reasons of mode choice (“business” and “bringing and picking up”) it is doubtful if it makes sense to pick only two out of one aggregate variable (reason for mode choice) actually consisting of more than three dummy variables. A likelihood ratio χ² test comparing the model including only mobility pattern with the one comprising trip related attributes as well as mobility pattern yields that the two (respectively three) additional variables do not improve the model significantly (Table 11-32). As a consequence, trip related attributes have been considered not to be relevant for the choice model and have been excluded from further analysis. As previously discussed, it is evident for all types of models that the corrected choice is more probable than the original choice resulting in a more reasonable approximation of data and model. 148 Table 11-31: Comparison of the results of the logit models including socio-demographic characteristics, trip related attributes and mobility pattern according to scenarios and choice samples Socio-demographic characteristics and trip related attributes and mobility pattern Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL Sociodemographic characteristics** Attributes of alternatives* Number of observations 355 355 354 1064 Log lik. -143.4633 -149.0380 -170.4278 -467.4424 Adjusted R² .35965 .33288 .61957 .65311 Log lik. -59.08211 -64.07668 -80.49319 -205.4809 Adjusted R² .73629 .71318 .82032 .84751 321 321 320 962 Log lik. -107.4964 -118.7742 -142.1862 -376.1012 Adjusted R² .45505 .39597 .64170 .68545 Log lik. -54.35840 -59.52863 -76.01741 -191.8473 Adjusted R² .72443 .69727 .8044 .83955 321 321 320 962 Log lik. -100.6095 -112.4224 -137.7633 -359.8022 Adjusted R² .48672 .4262 .65206 .69869 Log lik. -48.30863 -54.24428 -67.93084 -173.3553 Adjusted R² .75354 .72238 .82843 .85483 321 321 320 962 Log lik. -95.35112 -105.6851 -136.7774 -351.3971 Adjusted R² .51043 .45562 .65377 .70534 Log lik. -47.53024 -52.49112 -66.57966 -169.7942 Adjusted R² . 75596 .72962 .83147 .85762 Original choice Corrected choice Number of observations Original choice Corrected choice Mobility pattern*** Number of observations Original choice Corrected choice Trip related attributes and mobility pattern**** Number of observations Original choice Corrected choice * including attributes of alternatives (COST, TIME) and ASC (A_CAR) ** including attributes of alternatives (COST, TIME), socio-demographic characteristics (AGE, GENDER, INCOME) and ASC (A_CAR) *** including attributes of alternatives (COST, TIME), socio-demographic characteristics (AGE, GENDER, INCOME), mobility pattern (DESTINATION, TRANSIT) and ASC (A_CAR) **** including attributes of alternatives (COST, TIME), socio-demographic characteristics (AGE, GENDER, INCOME), mobility pattern (DESTINATION, TRANSIT), trip related attributes (BUSINESS, BRINGING) and ASC (A_CAR); without variable LENGTH 149 Table 11-32: Results of the likelihood ratio χ² tests comparing logit model including sociodemographic characteristics with model including attributes of alternatives and trip related attributes and mobility pattern according to scenarios (corrected choice) Likelihood ratio χ² test: Socio-demographic characteristics Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL u Attributes of alternatives – Socio-demographic characteristics Significance χ² u Socio-demographic characteristics – Mobility pattern Socio-demographic characteristics – Trip related attributes and mobility pattern o -2*(LL – LL ) Significance χ² u o -2*(LL – LL ) Significance χ² u Trip related attributes and mobility pattern – Mobility pattern o -2*(LL – LL ) 9,44742 9,0961 8,95156 27,2672 0,023897352 0,028040097 0,029941903 5,17481E-06 12,09954 10,5687 16,17314 36,984 0,002358404 0,005070327 0,000307643 9,31165E-09 13,65632 14,07502 18,8755 44,1062 0,008476713 0,007059432 0,000831465 6,09803E-09 1,55678 3,50632 2,70236 7,1222 0,459144651 0,319943328 0,439826393 0,068103169 o -2*(LL – LL ) Significance χ² The discussion of parameters of the choice models will be done exemplarily for Scenario B for socio-demographic characteristics (Table 11-33) and for Scenario C for sociodemographic characteristics and mobility pattern (Table 11-34). A look at the significances of the variables in Scenario B shows that TIME and the alternative specific constant (ASC) for car (A_CAR) are significant at the 5% level (α > 0.05), COST and M_AGE are significant at the 10% level (α > 0.1), whereas gender (M_MALE) and income (M_INC1) are not significant (Table 11-33). While the coefficient of TIME has the expected negative sign, COST is – as already seen in the previous models – positive. As the sociodemographic variables have been allocated to the alternative car, it can be assumed that male respondents and respondents with higher income rather use the car than CLEVER (although both are not significant), and that respondents with increasing age rather use CLEVER than car. However, the ASC has again the strongest significant influence on the utility of car. Commenting the parameters of the model including socio-demographic characteristics as well as mobility pattern for scenario C (Table 11-34) starts with the random influence of the variable COST, which is neither significant nor has it the expected negative sign. TIME has again a negative, significant influence on the choice of CLEVER – with increasing (car) time the probability to choose CLEVER rises. The results of the socio-demographic variables are quite similar to those of the previously discussed model. The variable transit (M_TRIP) is significant and may suggest that with increasing time in transit the probability to choose car increases, while the probability to choose one of the other three alternatives (CLEVER, PT, bicycle) decreases. The variable destination (M_DES) is not significant, while the ASC for car and CLEVER are significant and strongly influencing the utility function. The adjusted R² is with 0.82843 quite high. 150 Table 11-33: Results of the discrete choice estimation (weighted) including attributes of alternatives and socio-demographic characteristics according to scenarios (corrected mode choice) Scenario A,B,C Scenario C Scenario B Scenario A Scenario Variable Coefficient Standard Error t-value Significance Log likelihood COST .03729143 .08954107 .416 .6771 M_MALE .37431769 .62405972 .600 .5486 M_AGE -.03687567 .02117852 -1.741 .0817 M_INC1 .57298395 .38526039 1.487 .1369 A_CAR 3.48931527 1.32034860 2.643 .0082 COST .34134797 .20291968 1.682 .0925 TIME -.14594291 .05545799 -2.632 .0085 .73961045 .56783498 1.303 .1927 M_AGE -.03252881 .01967859 -1.653 .0983 M_INC1 .41430257 .32026200 1.294 .1958 A_CAR 3.44302679 1.17867129 2.921 .0035 COST .16547946 .10994368 1.505 .1323 TIME -.07895641 .03280343 -2.407 .0161 .67059894 .51524429 1.302 .1931 M_AGE -.02735022 .01829699 -1.495 .1350 M_INC1 .33165725 .29480569 1.125 .2606 A_CAR 6.21339136 1.68081975 3.697 .0002 A_CLEVER 3.17024404 1.29475603 2.449 .0143 A_PT .16114547 1.59792670 .101 .9197 COST .14547056 .07337601 1.983 .0474 TIME -.07876850 .02312923 -3.406 .0007 .62446704 .32328135 1.932 .0534 M_AGE -.03224474 .01133201 -2.845 .0044 M_INC1 .42848850 .18940292 2.262 .0237 A_CAR 6.42400728 1.43926897 4.463 .0000 A_CLEVER 3.17430137 1.26839426 2.503 .0123 .08982750 1.61171288 .056 .9556 M_MALE M_MALE M_MALE A_PT 151 Adjusted R² -54.35840 .72443 -59.52863 .69727 -76.01741 .8044 -191.8473 .83955 Table 11-34: Scenario Results of the discrete choice estimation (weighted) including attributes of alternatives, socio-demographic characteristics and mobility pattern according to scenarios (corrected mode choice) Variable COST Scenario A Scenario B t-value Significance Log likelihood .08585368 -1.391 .1642 .55866941 .66318232 .842 .3996 M_AGE -.05465742 .02329838 -2.346 .0190 M_INC1 .47870843 .37863458 1.264 .2061 M_DES .25119485 .40519007 .620 .5353 M_TRIP .02611674 .01098922 2.377 .0175 A_CAR 2.29056176 1.39843300 1.638 .1014 COST .27602700 .20859362 1.323 .1857 TIME -.17937655 .06362474 -2.819 .0048 .91240674 .60663136 1.504 .1326 M_AGE -.04526709 .02097950 -2.158 .0310 M_INC1 .33929836 .31264424 1.085 .2778 M_DES .37387475 .39421028 .948 .3429 M_TRIP .01929958 .00928834 2.078 .0377 A_CAR 2.28599600 1.24450042 1.837 .0662 COST .06275222 .10872699 .577 .5638 TIME -.10962087 .03925463 -2.793 .0052 .89507801 .55805683 1.604 .1087 M_AGE -.04091908 .01955054 -2.093 .0364 M_INC1 .20698754 .28551494 .725 .4685 M_DES .56515512 .39206041 1.442 .1494 M_TRIP .02068128 .00861686 2.400 .0164 A_CAR 5.08113631 1.86659048 2.722 .0065 A_CLEVER 3.37582080 1.42657378 2.366 .0180 A_PT .69651608 1.61812840 .430 .6669 COST .03923762 .06817882 .576 .5649 TIME -.10301651 .02692927 -3.825 .0001 .81477706 .34465441 2.364 .0181 M_AGE -.04586837 .01208652 -3.795 .0001 M_INC1 .32785971 .18319749 1.790 .0735 M_DES .43716171 .22768293 1.920 .0549 M_TRIP .02009594 .00521434 3.854 .0001 A_CAR 5.42603017 1.54628075 3.509 .0004 A_CLEVER 3.41183397 1.34360595 2.539 .0111 .68707129 1.59329961 .431 .6663 M_MALE M_MALE Scenario C Standard Erorr -.11942185 M_MALE M_MALE Scenario A,B,C Coefficient A_PT 152 Adjusted R² -48.30863 .75354 -54.24428 .72238 -67.93084 .82843 -173.3553 .85483 11.4.4 Attitudes and subjective motives Beside the previous presented objective variables, selected attitudinal variables are considered in the mode choice model, including dummies, for reasons of mode choice and for the assessment of CLEVER. The variables enter the model as alternative specific variables allocated to car choice (Table 11-35). They do not vary across the scenarios. Table 11-35: Definition of the variables “attitudes and subjective motives” Subjective motives (all alternative specific – car) Variables Definition Scale M_RECOST Dummy variable for the reason of mode choice with one for “cost advantage”. dummy M_RETIME Dummy variable for the reason of mode choice with one for “time advantage”. dummy M_CL_1 Dummy variable for the assessment of CLEVER with one for positive comments concerning “CLEVER Idea”. dummy M_CL_2 Dummy variable for the assessment of CLEVER with one for positive comments concerning “CLEVER Design”. dummy The utility specifications for the binomial logit model include attributes of alternatives for both alternatives car and CLEVER and additionally four selected variables specifying subjective motives: V(car ) = β car + β COST * COSTcar + β TIME * TIMEcar + β M _ RECOST * M _ RECOSTcar + β M _ RETIME * M _ RETIMEcar + β M _ CL _ 1 * M _ CL _ 1car + β M _ CL _ 2 * M _ CL _ 2 car V( CLEVER ) = β COST * COSTCLEVER + β TIME * TIMECLEVER KUPPAM A.R., PENDYALA R.M., RAHMAN S. (1999) found out modelling mode choice behaviour that “a model including exclusively attitudinal variables performed better than a model including exclusively demographic variables. Likewise a model including both demographic and attitudinal variables performed the best. … Likelihood ratio χ² tests showed that both demographic variables and attitudinal factors are important and significant in explaining mode-choice behaviour. However, the statistic testing the significance of attitudinal factors was found to be nearly twice the statistic testing the significance of demographic variables. This appears to indicate that that attitudinal factors are extremely important in explaining mode-choice behaviour and that their omission from mode-choice models may be more serious than the omission of demographic variables.” These findings can be supported by the actual research. Modelling only subjective motives (including attributes of alternatives) results in a slightly higher adjusted R² and a better log likelihood than the model comprising only socio-demographic characteristics (Table 11-36). Likelihood ratio χ² tests showed that the additional variables are verified for both models compared to the original model with only attributes of alternatives by a significant χ² (Table 11-37). However, the model including socio-demographic variables as well as subjective 153 motives has finally a better performance than the separate models – compare Table 11-37: Subjective motives – subjective motives incl. sociodemographic characteristics (e.g Scenario B, corrected choice, Significance χ² = 0,006946524). Table 11-36: Comparison of the results of the logit models subjective motives without … and … including socio-demograhic characteristics as well as socio-demographic characteristics according to scenarios and choice samples Subjective motives B C ABC Model Binomial Logit Binomial Logit MNL MNL socio-demographic charcteristics ** Subjective motives including Sociodemographic characteristics* socio-demographic charcteristics * A Subjective motives without Scenario Number of observations 355 355 354 1064 Log lik. -79.84031 -85.56113 -93.80410 -263.9756 Adjusted R² .63955 .61261 .78976 .80364 Log lik. -53.35428 -56.76140 -71.43020 -184.3719 Adjusted R² .75912 .74300 .83991 .86285 321 321 320 962 Log lik. -107.4964 -118.7742 -142.1862 -376.1012 Adjusted R² .45505 .39597 .64170 .68545 Log lik. -54.35840 -59.52863 -76.01741 -191.8473 Adjusted R² .72443 .69727 .8044 .83955 321 321 320 962 Log lik. -62.37308 -71.35673 -79.81367 -223.5603 Adjusted R² .67975 .63245 .79797 .81254 Log lik. -46.72617 -50.69573 -64.99127 -165.9425 Adjusted R² .76009 .73887 .83549 .86085 Original choice Corrected choice Number of observations Original choice Corrected choice Number of observations Original choice Corrected choice * including attributes of alternatives (COST and TIME) ** including attributes of alternatives (COST and TIME) and socio-demographic characteristics (AGE, GENDER, INCOME) Table 11-37: Results of the likelihood ratio χ² tests comparing logit model including subjective motives with attributes of alternatives and socio-demographic characteristics (corrected choice) according to scenarios Likelihood ratio χ² test: Subjective motives Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL u Attributes of alternatives – Subjective motives Significance χ² u Attributes of alternatives – Socio-demographic characteristics Subjective motives – subjective motives incl. sociodemographic characteristics o -2*(LL – LL ) 11,45566 14,63056 18,12598 42,218 0,02189324 0,005532114 0,001166059 1,5033E-08 9,44742 9,0961 8,95156 27,2672 0,023897352 0,028040097 0,029941903 5,17481E-06 13,25622 12,13134 12,87786 36,8588 0,004114039 0,006946524 0,004908325 4,92902E-08 o -2*(LL – LL ) Significance χ² u o -2*(LL – LL ) Significance χ² 154 Table 11-38: Scenario Results of the discrete choice estimation (weighted) (corrected mode choice) including attributes of alternatives and subjective motives according to scenarios Variable Scenario A,B,C Scenario C Scenario B Scenario A COST Coefficient Standard Error t-value Significance Log likeliood .04912595 .10168434 .483 .6290 -1.18366426 .58377782 -2.028 .0426 .25308891 1.17026967 .216 .8288 M_CL_1 -.74461948 .44672113 -1.667 .0955 M_CL_2 -.13037954 .35342048 -.369 .7122 A_CAR 5.91721411 1.46212537 4.047 .0001 COST .39567493 .17799150 2.223 .0262 TIME -.19065601 .05636536 -3.383 .0007 M_RECOST -.87407320 .56308153 -1.552 .1206 M_RETIME .02375206 1.18213331 .020 .9840 M_CL_1 -.73304689 .45559554 -1.609 .1076 M_CL_2 -.51966458 .35727263 -1.455 .1458 A_CAR 7.23507699 1.63433996 4.427 .0000 COST .19997727 .12209629 1.638 .1014 TIME -.09074100 .03559147 -2.550 .0108 M_RECOST -1.29125420 .52894216 -2.441 .0146 M_RETIME -.06584702 .69149797 -.095 .9241 M_CL_1 -.57259364 .40666874 -1.408 .1591 M_CL_2 -.24211845 .33632163 -.720 .4716 A_CAR 8.72204694 1.77911349 4.902 .0000 A_CLEVER 3.04669651 1.25818485 2.422 .0155 A_PT -.01670841 1.64931593 -.010 .9919 COST .18990330 .07986550 2.378 .0174 TIME -.09583499 .02489772 -3.849 .0001 -1.09870508 .31224536 -3.519 .0004 .02508784 .50874257 .049 .9607 M_CL_1 -.66010520 .24519456 -2.692 .0071 M_CL_2 -.27972861 .19717914 -1.419 .1560 A_CAR 9.09776342 1.47216375 6.180 .0000 A_CLEVER 3.10134614 1.24304972 2.495 .0126 A_PT -.17925505 1.69738080 -.106 .9159 M_RECOST M_RETIME M_RECOST M_RETIME Adjusted R² -53.35428 .75912 -56.76140 .74300 -71.43020 .83991 -184.3719 .86285 The parameters of the subjective motives resulting from the discrete choice estimation can be interpreted with reservation as the significance level of the four variables is rather poor (Table 11-38). Exemplarily scenario B of the models including attributes of alternatives as well as subjective motives is described: The negative sign in front of the coefficients 155 assessing CLEVER (M_CL_1, M_CL_2) indicates that those respondents who had a positive attitude towards the new vehicle are rather willing to choose CLEVER than those who gave negative ratings. Reasons for mode choice may suggest that reason cost is more important for CLEVER users than for car drivers, while it is contrary concerning reason time (but not significant). Although SP choice based on the figures COST and TIME indicates a different correlation as already discussed, this needs not necessarily be an inconsistency, as the cost argument has obviously not been that important for the original mode (car) choice and respondents seldom reveal their total reasons for their behaviour. The coefficient of the ASC again exceeds the other ones of the entering variables by far. Table 11-39: Results of the discrete choice estimation (weighted) including attributes of alternatives, socio-demographic characteristics and subjective motives according to Scenario B and Scenario C (corrected mode choice) Scenario Variable Scenario B Standard Error t-value Significance Log likelihood COST .37473306 .18973014 1.975 .0483 TIME -.19077609 .06156383 -3.099 .0019 .26302205 .64050214 .411 .6813 M_AGE -.06279094 .02236208 -2.808 .0050 M_INC1 .30517455 .35893606 .850 .3952 M_RECOST -.92072976 .62935375 -1.463 .1435 M_RETIME -.07933092 1.23784932 -.064 .9489 M_CL_1 -1.01189537 .57513721 -1.759 .0785 M_CL_2 -.65876305 .43473634 -1.515 .1297 A_CAR 10.9691356 2.74756857 3.992 .0001 COST .12530111 .10211617 1.227 .2198 TIME -.06875949 .03150394 -2.183 .0291 .54751983 .56595553 .967 .3333 M_AGE -.06892784 .02262462 -3.047 .0023 M_INC1 .05807532 .31236660 .186 .8525 M_RECOST -1.70055529 .60546061 -2.809 .0050 M_RETIME -.12756647 .70915908 -.180 .8572 M_CL_1 -.64888756 .49288314 -1.317 .1880 M_CL_2 -.35149828 .40951141 -.858 .3907 A_CAR 12.6554405 2.57446219 4.916 .0000 A_CLEVER 3.23950627 1.27207545 2.547 .0109 .05697982 1.64273813 .035 .9723 M_MALE M_MALE Scenario C Coefficient A_PT Adjusted R² -50.69573 .73887 -64.99127 .83549 Having a look at the parameters of the model including attributes of alternatives, sociodemographic characteristics as well as subjective motives exemplarily for Scenario C (Table 11-39), yields the following significant variables: TIME, AGE, RECOST, ASC car and ASC CLEVER. Comparing the coefficients of TIME and AGE shows that one minute (TIME) is equivalent to one year (AGE) entering the model with nearly the same coefficient. The 156 reason cost (RECOST) applies to CLEVER users, indicated by the negative sign, and is obviously stronger than the other significant variables – except the ASC for car and CLEVER, which are again the most effective ones. Compared to the model without sociodemographic variables the adjusted R² are quite the same, while the log likelihood indicates a better performance, which is also verified by the significant χ². 11.4.5 All selected variables The assembly of all selected variables of the presented groups (except for trip related attributes) results in one mode choice model for each scenario. The question is if this total model is the one with the highest explanatory power. Compared to the original model (only including attributes of alternatives) the additional variables improve the performance of the model significantly, verified by the significant χ² and an at least slight rise of the adjusted R², which is originally definitely high and for all scenarios in a quite good range (Table 11-40). It is noticeable that the difference between the results of the original and the corrected choice is clearly high in the fractional models, while in the model including all selected variables this difference gets quite narrow. Generally, it can be said that all four classes contribute the most promising variables to the mode choice model, improving the performance of the model at all. Table 11-40: Comparison of the results of the logit models attributes of alternatives and all selected variables according to scenarios and choice samples via likelihood ratio χ² test All selected variables Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL Model with attributes of alternatives Number of observations 355 355 354 1064 Log lik. -143.4633 -149.0380 -170.4278 -467.4424 Adjusted R² .35965 .33288 .61957 .65311 Log lik. -59.08211 -64.07668 -80.49319 -205.4809 Adjusted R² .73629 .71318 .82032 .84751 321 321 320 962 Log lik. -50.41991 -58.61293 -66.71887 -185.4160 Adjusted R² .73945 .69613 .83073 .84432 Log lik. -39.55854 -44.33020 -54.13423 -143.1722 Adjusted R² .79558 .77018 .86266 .87979 39,04714 39,49296 52,71792 124,6174 1,12973E-05 9,38604E-06 3,30581E-08 1,51229E-22 Original choice Corrected choice All selected variables* Number of observations Original choice Corrected choice Likelihood ratio χ² test: all selected variables (corrected choice) u Attributes of alternatives – All selected variables* o -2*(LL – LL ) Significance χ² * including attributes of alternatives, socio-demographic characteristics, mobilty pattern, subjective motives 157 Table 11-41 and Table 11-42 give the parameters resulting from the discrete choice estimation including all selected variables according to the scenarios. The significance of the parameters varies across the scenarios, e.g. while in scenario B variable COST is significant, it is not in scenario C, which may be caused by the additional alternatives (PT and bicycle). TIME, AGE and ASC car and ASC CLEVER are the only variables which are significant in all scenarios. Additionally, the signs of the coefficients differ as well across the scenarios (compare COST or INCOME). The omission of either one or more (non-significant) variables could be tested but may cause a shift of the remaining variables. Table 11-41: Results of the discrete choice estimation (weighted) including all selected variables according to scenarios A and B (corrected mode choice) Scenario A Scenario Variable Standard Error t-value Significance Log likelihood COST -.12319631 .10771185 -1.144 .2527 M_MALE -.26200796 .87200739 -.300 .7638 M_AGE -.07358800 .02750230 -2.676 .0075 M_INC1 .00918552 .44889389 .020 .9837 M_DES .46230129 .41067063 1.126 .2603 M_TRIP .03509309 .01467348 2.392 .0168 -1.26292663 .74362998 -1.698 .0894 .69891675 1.43456466 .487 .6261 M_CL_1 -1.35652418 .76114775 -1.782 .0747 M_CL_2 -.39936354 .49837415 -.801 .4229 A_CAR 9.26600261 2.81595404 3.291 .0010 COST .38169828 .19037497 2.005 .0450 TIME -.24691493 .07895371 -3.127 .0018 .16515375 .78661237 .210 .8337 M_AGE -.06945341 .02478751 -2.802 .0051 M_INC1 .08101739 .38607374 .210 .8338 M_DES .51137885 .46138520 1.108 .2677 M_TRIP .02343063 .01173321 1.997 .0458 M_RECOST -.98258091 .71153157 -1.381 .1673 M_RETIME .10235550 1.36596119 .075 .9403 M_CL_1 -.95969262 .66063834 -1.453 .1463 M_CL_2 -.87924516 .48521160 -1.812 .0700 A_CAR 9.87333893 2.79188806 3.536 .0004 M_RECOST M_RETIME M_MALE Scenario B Coefficient 158 Adjusted R² -39.55854 .79558 -44.33020 .77018 Table 11-42: Results of the discrete choice estimation (weighted) including all selected variables according to Scenario C and Scenario A;B,C (corrected mode choice) Scenario Variable Scenario C Standard Error t-value Significance Log likelihood COST .04190603 .09384215 .447 .6552 TIME -.11613610 .04360346 -2.663 .0077 .39871619 .68085567 .586 .5581 M_AGE -.07374155 .02278991 -3.236 .0012 M_INC1 -.28053772 .31385659 -.894 .3714 M_DES .86717915 .43047046 2.014 .0440 M_TRIP .02418230 .01031203 2.345 .0190 -2.00887005 .63559072 -3.161 .0016 .40233440 .80664175 .499 .6179 M_CL_1 -.66998546 .55438448 -1.209 .2268 M_CL_2 -.71943189 .43910129 -1.638 .1013 A_CAR 12.0971619 2.83253574 4.271 .0000 A_CLEVER 3.37872382 1.47464754 2.291 .0220 A_PT .80066102 1.65113030 .485 .6277 COST .06983850 .07514058 .929 .3527 TIME -.11564394 .03161407 -3.658 .0003 .20580095 .42782069 .481 .6305 M_AGE -.06932020 .01390865 -4.984 .0000 M_INC1 -.09027821 .20974968 -.430 .6669 M_DES .67109964 .24859391 2.700 .0069 M_TRIP .02297894 .00635429 3.616 .0003 -1.39417214 .36826509 -3.786 .0002 .31367824 .56193519 .558 .5767 M_CL_1 -.90337462 .34123628 -2.647 .0081 M_CL_2 -.60434690 .25505462 -2.369 .0178 A_CAR 12.2914316 2.07976198 5.910 .0000 A_CLEVER 3.68298767 1.44801254 2.543 .0110 .91717251 1.63442660 .561 .5747 M_MALE M_RECOST M_RETIME M_MALE Scenario A,B,C Coefficient M_RECOST M_RETIME A_PT Adjusted R² -54.13423 .86266 -143.1722 .87979 11.4.6 Influence of awareness One huge challenge of surveying hypothetical behaviour is the compliance of stated and real, prospective behaviour and mode choice respectively. To come up with this demand the original mode choice has been revised to the corrected one, which actually yields satisfying results in the previous discrete choice estimations. The hypothesis is that respondents get aware of the consequences owning or having a CLEVER available only after the SP part of the survey, which means that the decision to own CLEVER was made after the decision to use it for a trip, leading to a reversal of mode/CLEVER choice (compare chapter 11.2). To 159 determine this gap between original and corrected choice the two samples have been merged (doubled) and an additional variable indicating this influence of awareness has been generated (Table 11-43). Table 11-43: Definition of the variable defining influence of awareness Influence of awareness Variable AWARENESS INFO_C Definition Scale Alternative specific variable indicating the influence of awareness in the corrected choice sample (= 1 for alternative car in the corrected sample). dummy The new data set has been modelled again with the previous selected variables primarily without an additional awareness variable and following including the awareness dummy in the utility specification of the logit model. V( car ) = β car + β COST * COSTcar + β TIME * TIMEcar + β INFO _ C * INFO _ Ccar The results of the discrete choice estimation pursuing the different types of models reveal a marginal rise of the adjusted R² (Table 11-44 and Table 11-45), while the likelihood ratio χ² tests indicate a significant improvement of the models including the additional awareness variable (Table 11-46). Looking at the detailed parameters, exemplarily for scenario B and C and the models “Attributes of alternatives” (Table 11-47) and “All selected variables” (Table 11-48 and Table 11-49), a high significance of the new variable (INFO_C) can be detected in all cases as well as the expected positive coefficient. That means that the probability to choose car is more likely than to choose CLEVER with presence of the awareness variable. This approach verifies the influence of awareness on the original mode choice and justifies the revision of the original mode choice resulting in the corrected choice sample. Table 11-44: Comparison of the results of the logit models without and including an awareness variable according to scenarios (1) Influence of awareness (1) (sample: original and corrected choice merged) Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL 710 710 708 2128 Log lik. -215.2600 -227.3235 -267.7530 -715.5920 Adjusted R² .52095 .49339 .70191 .73487 Log lik. -202.6509 -214.8176 -252.2561 -675.3036 Adjusted R² .54837 .52058 .71902 .74972 642 642 640 1924 Log lik. -182.3981 -198.9496 -241.4113 -630.2167 Adjusted R² .54130 .49889 .69718 .73715 Log lik. -168.9896 -185.7604 -225.8269 -588.4617 Adjusted R² .57435 .53137 .71657 .75448 Attributes of alternatives Number of observations Without awareness variable Including awareness variable (INFO_C) Sociodemographic characteristics Number of observations Without awareness variable Including awareness variable (INFO_C) 160 Comparison of the results of the logit models without and including an awareness variable according to scenarios (2) Table 11-45: Mobility pattern* Influence of awareness (2) (sample: original and corrected choice merged) 642 642 640 1924 Log lik. -171.7350 -189.1883 -233.4404 -603.3420 Adjusted R² .56676 .52197 .70685 .74819 Log lik. -157.6242 -175.4794 -217.3489 -559.9633 Adjusted R² .60173 .55591 .72691 .76622 642 642 640 1924 Log lik. -139.4435 -151.6354 -179.8695 -480.3499 Adjusted R² .64711 .61565 .77387 .79939 Log lik. -121.9827 -134.5243 -157.9168 -424.8169 Adjusted R² .69081 .65848 .80136 .82253 642 642 640 1924 Log lik. -124.6075 -137.8246 163.1117 -434.8750 Adjusted R² .68365 .64954 .79471 .81827 Log lik. -104.9829 -119.0299 138.5154 -372.7743 Adjusted R² .73305 .69685 .82557 .84417 Number of observations Without awareness variable Including awareness variable (INFO_C) Number of observations Subjective motives* Without awareness variable Including awareness variable (INFO_C) All selected variables Number of observations Without awareness variable Including awareness variable (INFO_C) * Including attributes of alternatives (COST and TIME) and socio-demographic characteristics (M_MALE, M_AGE, M_INC1) Table 11-46: Results of the likelihood ratio χ² tests comparing the logit models without and including an awareness variable according to scenarios Likelihood ratio χ² test: Influence of awareness Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL u o -2*(LL – LL ) Attributes of alternatives Significance χ² u Socio-demographic characteristics 25,2182 25,0118 30,9938 80,5768 5,11968E-07 5,69805E-07 2,58854E-08 2,79623E-19 26,817 26,3784 31,1688 83,51 2,23661E-07 2,80662E-07 2,36537E-08 6,33945E-20 28,2216 27,4178 32,183 86,7574 1,0819E-07 1,63916E-07 1,40313E-08 1,22684E-20 34,9216 34,2222 43,9054 111,066 3,43252E-09 4,91649E-09 3,44637E-11 5,72332E-26 39,2492 37,5894 49,1926 124,2014 3,73021E-10 8,73177E-10 2,32024E-12 7,61126E-29 o -2*(LL – LL ) Significance χ² u o -2*(LL – LL ) Mobility pattern* Significance χ² u o -2*(LL – LL ) Subjective motives* Significance χ² u o -2*(LL – LL ) All selected variables Significance χ² * Including attributes of alternatives (COST and TIME) and socio-demographic characteristics (M_MALE, M_AGE, M_INC1) 161 Table 11-47: Scenario C: with information dummy Scenario C Sc. B: with information dummy Scenario B Model Results of the discrete choice estimation (weighted, merged original and corrected choice) including attributes of alternatives COST and TIME plus information dummy, exemplary for scenario B and C Variable t-value Significance Log likelihood .11319338 .05265311 2.150 .0316 TIME -.08221270 .02463689 -3.337 .0008 A_CAR 2.23873725 .16707546 13.400 .0000 COST .11789994 .05364233 2.198 .0280 TIME -.08669843 .02542289 -3.410 .0006 INFO_C 1.33132127 .28680362 4.642 .0000 A_CAR 1.75045288 .18404934 9.511 .0000 COST .19323958 .06095069 3.170 .0015 TIME -.06908260 .02031355 -3.401 .0007 A_CAR 5.44881238 .85875708 6.345 .0000 A_CLEVER 3.67283923 .85895504 4.276 .0000 A_PT .20489383 1.10129221 .186 .8524 COST .19996282 .06217558 3.216 .0013 TIME -.07117700 .02066549 -3.444 .0006 INFO_C 1.37203390 .26514117 5.175 .0000 A_CAR 4.92227694 .86460257 5.693 .0000 A_CLEVER 3.67419632 .86102888 4.267 .0000 .19809318 1.10239724 .180 .8574 Table 11-48: Scenario B: all selected variables including information dummy Standard Error COST A_PT Model Coefficient Adjusted R² -227.3235 .49339 -214.8176 .52058 -267.7530 .70191 -252.2561 .71902 Results of the discrete choice estimation (weighted, merged original and corrected choice) including all selected variables plus information dummy, exemplarily for scenario B Variable Coefficient Standard Error t-value Significance Log likelihood COST .25193924 .10473203 2.406 .0161 TIME -.16762717 .04377447 -3.829 .0001 M_MALE -.27026476 .45790761 -.590 .5550 M_AGE -.04111434 .01492829 -2.754 .0059 M_INC1 .47175630 .25773080 1.830 .0672 M_DES .47796814 .20767162 2.302 .0214 M_TRIP .01483770 .00522402 2.840 .0045 M_RECOST -2.60592106 .41980458 -6.207 .0000 M_RETIME -.60189853 .61134030 -.985 .3248 M_CL_1 -1.04627782 .39041732 -2.680 .0074 M_CL_2 -.82418832 .28529747 -2.889 .0039 INFO_C 2.10773259 .38505356 5.474 .0000 A_CAR 7.25342842 1.62121847 4.474 .0000 162 -119.0299 Adjusted R² .69685 Table 11-49: Scenario C: all selected variables including information dummy Model Results of the discrete choice estimation (weighted, merged original and corrected choice) including all selected variables plus information dummy, exemplarily for scenario C Variable Coefficient Standard Error t-value Significance Log likelihood COST .18250633 .08473286 2.154 .0312 TIME -.08138826 .02952059 -2.757 .0058 M_MALE -.46373209 .42148585 -1.100 .2712 M_AGE -.04449866 .01477724 -3.011 .0026 M_INC1 -.01926999 .21956777 -.088 .9301 M_DES .58171384 .17641463 3.297 .0010 M_TRIP .01281185 .00451551 2.837 .0045 -3.27569000 .43394531 -7.549 .0000 .14991770 .47222699 .317 .7509 M_CL_1 -.48021034 .35317318 -1.360 .1739 M_CL_2 -1.18635326 .28434350 -4.172 .0000 INFO_C 2.36860112 .38585141 6.139 .0000 A_CAR 11.1294099 1.76942353 6.290 .0000 A_CLEVER 3.78393803 .89671289 4.220 .0000 .27962182 1.11177009 .252 .8014 M_RECOST M_RETIME A_PT Adjusted R² -138.5154 .82557 11.4.7 Methodological influence Usually the design of the survey as well as the interview itself should be completely objective and not affect the respondents’ answers and SP decisions at all. Nevertheless, it can not be ruled out that certain methodical influences may bias respondents’ choices. In the actual case a possible influence of the duration of the interview and of the interviewer (by means of the number of interviews an interviewer has done so far, which may reflect the interviewer’s routine) are tested including these two selected variables in the logit model (Table 11-50). They enter the utility specifications as alternative specific variables allocated to alternative car: V( car ) = β car + β COST * COSTcar + β TIME * TIMEcar + β M _ INT _ NO * M _ INT _ NOcar + β M _ INTMIN * M _ INTMIN car Table 11-50: Definition of the variables for methodological influence Methodological influence Variables Definition Scale metric INTERVIEW NUMBER M_INT_NO Number of interview an interviewer has done so far in ascending order (alternative specific – car). INTERVIEW M_INTMIN Duration of the interview in minutes (alternative specific – car). metric 163 Table 11-51: Comparison of the results of the logit models without and including methodical influence according to scenarios (corrected choice) Methodological influence (sample: corrected mode choice) Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL 355 355 354 1064 Log lik. -59.08211 -64.07668 -80.49319 -205.4809 Adjusted R² .73629 .71318 .82032 .84751 Log lik. -53.18147 -57.01326 -76.81150 -190.2865 Adjusted R² .76127 .74334 .82819 .85862 321 321 320 962 Log lik. -54.35840 -59.52863 -76.01741 -191.8473 Adjusted R² .72443 .69727 .8044 .83955 Log lik. -46.13888 -50.21997 -69.57921 -168.3824 Adjusted R² .76461 .74297 .82427 .85899 321 321 320 962 Log lik. -48.30863 -54.24428 -67.93084 -173.3553 Adjusted R² .75354 .72238 .82843 .85483 Log lik. -42.76594 -47.08755 -63.77055 -157.1466 Adjusted R² .78042 .75746 .83858 .86823 321 321 320 962 Log lik. -46.72617 -50.69573 -64.99127 -165.9425 Adjusted R² .76009 .73887 .83549 .86085 Log lik. -41.36620 -42.24141 -59.32849 -147.0688 Adjusted R² .78624 .78101 .84948 .87652 321 321 320 962 Log lik. -39.55854 -44.33020 -54.13423 -143.1722 Adjusted R² .79558 .77018 .86266 .87979 Log lik. -37.68712 -38.62384 -51.63751 -134.1413 Adjusted R² .80399 .79846 .86870 .88722 Attributes of alternatives Number of observations without methodical variables including methodical influence Mobility pattern* Sociodemographic characteristics Number of observations without methodical variables including methodical influence Number of observations without methodical variables including methodical influence Subjective motives* Number of observations without methodical variables including methodical influence All selected variables Number of observations without methodical variables including methodical influence * including attributes of alternatives (COST and TIME) and socio-demographic characteristics (M_MALE, M_AGE, M_INC1) A comparison of the results of the discrete choice estimation shows a slight improvement of the adjusted R² for those models which include the variables of methodical influence (Table 11-51). The likelihood ratio χ² tests reveal a significant improvement of the models including the selected methodical variables, so they do add explanatory power (Table 11-52). The detailed parameters are given in Table 11-53 exemplarily for the model including attributes of alternatives and the one including all selected variables both for scenario B. In both cases the variable indicating the number of interviews an interviewer has done so far (M_INT_NO) is significant with a positive coefficient. That means that with increasing routine an interviewer has, the choice of car is more likely than the probability to choose CLEVER. This 164 result could be explained by the assumption that the interviewer’s own lively interest for the CLEVER, which s/he gets across in his/her first interviews, sags and influences the respondent’s choice. For the model including all selected variables the second methodical variable, the duration of the interview (M_INTMIN), is also significant but with a negative coefficient. That indicates that with increasing interview time the probability to choose CLEVER rises. One explanation may be that more information of CLEVER, imparted in a longer time period, leads to more acceptance of the new vehicle. On the other hand one can assume that with increasing interview time, the respondent tries to cooperate with the interviewer and therefore rather chooses the new vehicle. Table 11-52: Results of the likelihood ratio χ² tests comparing the logit models without and including methodical variables according to scenarios (corrected choice) Likelihood ratio χ² test: Methodological influence (corrected choice) Scenario A B C ABC Model Binomial Logit Binomial Logit MNL MNL u o -2*(LL – LL ) Attributes of alternatives Significance χ² u Socio-demographic characteristics o -2*(LL – LL ) Significance χ² u o -2*(LL – LL ) Mobility pattern* Significance χ² u o -2*(LL – LL ) Subjective motives* Significance χ² u 11,80128 14,12684 7,36338 30,387 0,0005919 0,0001709 0,0066566 3,53892E-08 16,43904 18,61732 12,8764 46,9298 5,02398E-05 1,59762E-05 0,000332752 7,35754E-12 11,08538 14,31346 8,32058 32,4174 0,00087011 0,000154754 0,00391984 1,24368E-08 10,71994 16,90864 11,32556 37,7474 0,001059871 3,92226E-05 0,000764475 E-108,0524 3,74284 11,41272 5,51826 18,0618 0,053034244 0,00072943 0,01881897 2,13849E-05 o -2*(LL – LL ) All selected variables Significance χ² * including attributes of alternatives (COST and TIME) and socio-demographic characteristics (M_MALE, M_AGE, M_INC1) 165 Table 11-53: All selected variables Attributes of alternatives Model Results of the mode choice estimation (weighted) for Scenario B including attributes of alternatives COST and TIME plus methodological influence Variable Coefficient Standard Error t-value Significance Log likelihood COST .27637948 .17541529 1.576 .1151 TIME -.17425212 .05741634 -3.035 .0024 M_INT_NO .19119601 .07152845 2.673 .0075 M_INTMIN -.00612751 .01085198 -.565 .5723 A_CAR 2.71857939 .79508257 3.419 .0006 COST .34040663 .20964621 1.624 .1044 TIME -.29181883 .09895161 -2.949 .0032 M_MALE 1.27685833 .89781014 1.422 .1550 M_AGE -.08775707 .02804723 -3.129 .0018 M_INC1 .27137782 .44293263 .613 .5401 M_DES -.06208184 .47047662 -.132 .8950 M_TRIP .02634665 .01167428 2.257 .0240 M_RECOST -1.02208619 .74057912 -1.380 .1676 M_RETIME -1.84238148 1.63416676 -1.127 .2596 M_CL_1 -.08499745 .70857312 -.120 .9045 M_CL_2 -1.57796430 .61112312 -2.582 .0098 M_INT_NO .21618111 .10177363 2.124 .0337 M_INTMIN -.04297154 .01908043 -2.252 .0243 A_CAR 12.1254727 3.30781202 3.666 .0002 Adjusted R² -57.01326 .74334 -38.62384 .79846 11.5 Constraints of the model In spite of quite acceptable results of the models, there is no doubt about some constraints appearing during the modelling procedure. They can be traced back to at least three stages of the research: – Design of the SP survey (Lacking of orthogonality of attributes of alternatives; basis for the hypothetical mode choice: each single trip per day and person, a trip chain or only one trip per person; Scenario assumptions; well directed questions); – Choice of the respondents (including decision rule determined by the analyst) and – Selection of variables for modelling. These issues overlap to some extent and are crucial for the quality of the model output. As the SP design was based on the trip diary of the respondents, the hypothetical mode choice was made for each single trip of one day separately, regardless their dependencies, according to the three different scenarios. That resulted in a large number of trips per person and in very small differences of both crucial attributes of alternatives travel costs and travel time between car and CLEVER trips, which probably constrained a clear decision of the respondents due to the hypothesis of utility maximation. A more explicit distinction of the three scenarios might have led to major ranges of travel costs and travel time. Consequently, 166 a more deliberate consideration of orthogonality would have been desirable leading to more significant modelling results. Additionally, it would have been worth considering the basis for the hypothetical mode choice, which may not necessarily be each single trip, but a trip chain or even a trip purpose; or as well the hypothetical purchase decision could have been designed. Well-directed questions would have eased the definition of variables already in the stage of survey design. The choice process itself implicates a decision rule, which “describes the internal mechanisms used by the decision maker to process the information available and arrive at a unique choice [BEN-AKIVA M., LERMAN S.R. (1985)].” The logit model is based on the assumption of utility maximation, which “assumes commensurability of attributes”. The decision maker attempts to make his/her choice based on the maximium of attractiveness or utility of one mode in comparing different attributes. However, there are some more decision rules proposed in the literature [BEN-AKIVA M., LERMAN S.R. (1985)], one of them is dominance: “An alternative is dominant with respect to another if it is better for at least one attribute and no worse for all other attributes. … At best this rule can be used to eliminate inferior alternatives from a choice set. … In most realworld decisions there are many attributes of relevance to each decision maker, and it is very rare to find an alternative that is dominant over all attributes. Additional complexity may be introduced by an assumption (of a range of indifference or a threshold level) for each attribute. In other words, for one alternative to be better than another, the difference in the attribute values must exceed a threshold. Thus a travel time difference of five minutes or less, for example, may be considered by a decision maker too small to make a difference in his or her preference ranking of alternative modes.” That confirms the assumptions above concerning the differences of attributes. “It is worth noting that not all observed choice behaviour is an outcome of such an explicit decision-making process. An individual can, for example, follow a habit, assume some form of conventional behavior, follow intuition,… However, these forms of behavior can be represented as a choice process in which the decision maker generates only one alternative [BEN-AKIVA M., LERMAN S.R., p.32/33 (1985)]”. That leads to the importance of attitudes, perceptions, information, constraints, habits etc. to be added to the model [BRADLEY M. (2004)], which is based on the viewpoint that “…much of the motivation (‘drive’) underlying our behaviour is unconscious, especially to ourselves. If this is so, respondents cannot always tell us accurately why they make certain choices.” Although some of these ‘soft’ variables have actually been collected in the SP survey and been entered to the model, a more deliberate and explicit attendance should have been provided in the SP design. The selection of variables to be entered into the model is actually another source of error. Although a systematic approach should have ensured the selection of the most promising variables, it can not be ruled out that possibly some vital ones have been omitted (e.g. number of persons or kids per household). Another point of critique affects the logit model itself, “which tends to reproduce the decisions of individuals only at a certain point of time. The logarithm of decision of traditional transportation models has to be calibrated newly every time for the respective application and sample by means of relevant, given parameters. … It is a snap-shot appropriate to describe a specific situation related to significant conditions at a certain moment [WALTHER K. (2000)]”. 167 12 Benefits for the Environment and for Urban Traffic What are the consequences of the mode shift towards CLEVER for society, economy and for the environment and what are the benefits for urban traffic respectively? The benefits for urban traffic caused by the use of CLEVER are evaluated by means of a cost benefit analysis (CBA) based on the hypothetical change of the modal split in the surveyed scenarios in Graz [CLEVER 2005]. Excursus: Basic principles of a cost benefit analysis In general economic costs are defined as a monetary evaluation of the consumption of resources. Economic resources consumed in context with transport issues are for example the construction and maintenance of transport infrastructure, harm and damages to persons and nature caused by traffic, time consumption etc. Payment flows between groups of people and organisations (e.g. taxes paid by car drivers) are not considered. Depending on if economic resources are traded on a market or not different problems arise for particular cost components. For the evaluation of resources traded on a market the available market prices are used. Taxes and duties are not considered as they are transfer payments and not consumable resources. The consumption of natural resources, which are not traded on markets, causes an enormous economic loss. Those “negative external effects” of production and consumption activities are seldom borne by the originator, but cause costs for the community. Negative external effects comprise among others CO2 emissions and emissions of hazardous air pollutants, noise costs and to some extent accident costs. Evaluating non-market goods mainly the damage, which is caused, is estimated. Thereby damage caused to the environment is evaluated due to the dose-effect relationship by means of calculating the costs for the regeneration or loss of things or human health [PISCHINGER et al. 1998]. The calculation of the CBA is based on the weighted and projected data resulting from the RP and SP survey in Graz, considering the amount of travel of the inhabitants of Graz. The mode shift in the three scenarios (compare chapter 9.2 and 9.3) and the resulting change in kilometres travelled by individual motorised modes (car driver, moped/motorcycle, CLEVER) (Table 12-1) cause a change of the regarded values considered for the evaluation of the benefits for urban traffic. The traffic volume is estimated by means of a descriptive analysis projecting the (in the RP) reported trip length per day to the traffic volume per year in the city of Graz for the actual state 2003 and depending on mode choice in the three scenarios. The values referred to in the CBA comprise CO2 emissions and emissions of hazardous air pollutants, fuel consumption and running costs, noise, road accidents, journey time, parking infrastructure and welfare losses (Figure 12-1). The costs are calculated for the year 2003 (rate of inflation considered) for the actual state and under scenario conditions based on the travel amount of the inhabitants of Graz. The period under consideration is one year for all sub-issues. 168 Input Modal Shift in the 3 Scenarios Traffic volume [vehicle-km] Air Pollutants & CO2-emissions Noise Costs Values Fuel consumption & Running costs Road accidents Journey time Parking Infrastructure Welfare Losses Figure 12-1: Approach of the CBA Table 12-1: Traffic volume (kilometres travelled per day) [km/day] (including car driver, motorcycle/moped and CLEVER trips) in Graz in the actual state and in the three scenarios, 2003 Traffic volume Actual state 2003 Car driver, motorcycle/moped [km/day] 4.068.800 km 4.068.800 km Scenario B Scenario C 4.008.200 km 3.991.500 km 3.983.100 km 116.100 km 163.200 km 158.700 km 4.124.300 km 4.154.700 km 4.141.800 km + 55.500 km + 85.900 km + 73.000 km + 1,37 % + 2,11 % + 1,80 % – CLEVER [km/day] Sum Scenario A Absolute difference to the actual state Relative difference to the actual state [%] 12.1 Hazardous Air Pollutants and CO2-Emissions 12.1.1 Basics for the calculation Evaluating the pollutant emissions a distinction is made between the local effective hazardous air pollutants (CO, HC, NOx, SO2, particles) and the global effective CO2emissions. The emissions caused by car, moped/motorcycle and CLEVER trips are taken into account. Basis for the calculation of the emissions are the weighted and projected trips made by individual motorised modes like car (driver) or moped/motorcycle as a result of the RP survey. Since the velocities of the individual car and motorcycle trips, which are considered in the calculations, are estimations of the respondents, calculated from the information about trip length and travel time, they have been checked according to their plausibility and corrected if necessary. 169 Car and Motorcycle Based on the emission curves for collectives of vehicles (Figure 12-2) from the recommendations for economic feasibility studies for roads (EWS) [FGSV 1997] the emission factors for the different pollutants are calculated for petrol and diesel cars related to speed [km/h] using a regression model according to the formula given below. EFVG , j (V ) = (c0 + c1 *V 2 + c2 ) V for V > 20 km/h c ⎫ ⎧ for V ≤ 20 km/h EFVG , j (V ) = min ⎨cs , (c 0 + c1 *V 2 + 2 )⎬ V ⎭ ⎩ Emission factor of the pollutant j of the vehicle group VG EFVG , j (V ) against speed V [g/(km*vehicle)] V c0 Average speed per VG Parameter [g/vehicle-km] c1 Parameter [g*h2/km3] c2 Parameter [g/h] cs Emission factor cs for congestion within built-up areas [g/vehicle-km] [km/h] CO emissions against speed HC emissions against speed 12,00 1,20 Car - Petrol Car - Petrol 1,00 Car - Diesel Emissions in [g/car-km] Emissions in [g/car-km] 10,00 8,00 6,00 4,00 Car - Diesel 0,80 0,60 0,40 0,20 2,00 0,00 0,00 0 10 20 30 40 50 60 70 0 80 10 20 30 40 50 60 70 80 Speed [km/h] Speed [km/h] SO2 emissions against speed NOx emissions against speed 0,08 0,60 Car - Petrol 0,50 Car - Petrol 0,07 Car - Diesel Car - Diesel Emissions in [g/car-km] Emissions in [g/car-km] 0,06 0,40 0,30 0,20 0,05 0,04 0,03 0,02 0,10 0,01 0,00 0,00 0 10 20 30 40 50 60 70 80 0 10 20 Speed [km/h] 30 40 50 60 70 80 Speed [km/h] Particles against speed CO2 emissions against speed 0,12 600 Car - Petrol Car - Petrol Car - Diesel 500 0,08 Emissions in [g/car-km] Emissions in [g/car-km] 0,10 0,06 0,04 Car - Diesel 400 300 200 100 0,02 0 0,00 0 10 20 30 40 50 60 70 0 80 Figure 12-2: 10 20 30 40 50 60 70 80 Speed [km/h] Speed [km/h] Emissions related to speed according to petrol and diesel cars for a passenger car collective for 2002 [FGSV 1997] 170 Table 12-2 gives the parameters c0, c1 and c2 of the emission factors for petrol and diesel cars for each relevant pollutant j. At congestion within built-up areas (velocities less than 20 km/h) constant emission loads are assumed according to Table 12-3. Reduction factors (Table 12-4) consider the change of the composition of the collective of vehicles after the year 1990 and are taken for 2003. The allocation of the trips in the dataset according to petrol and/or diesel cars is done at random due to the ratio 53,49 : 46,51 for Austria [STATISTIK AUSTRIA 2004]. Table 12-2: Parameters c0, c1 and c2 of the emission factors (related to speed) for petrol and diesel cars, 1990 [FSGV 1997] Car - petrol Car - diesel Pollutant c0 c1 CO -2,0002 0,0008431 299,98 0,0574 0,000017 24,24 HC 0,275764 0,000019585 32,9727 -0,044245 0,000002796 7,9606 NOx 0,988995 0,00011532 8,2855 0,295481 0,000024449 11,92 SO2 0,006751 0,000000897 0,5555 0,058791 0,000006394 3,5314 - - - 0,00614 0,000010555 3,6378 55,9963 0,0074359 4.604,89 59,7388 0,0064969 3.588,39 Particles CO2 Table 12-3: c2 c0 c1 c2 Emission factor cs [g/vehicle-km] at congestion within built-up areas for petrol and diesel cars, 1990 [FSGV 1997] cs Pollutant Car - petrol Car - diesel CO 31,084 1,680 HC 5,049 0,365 NOx 1,234 1,378 SO2 0,066 0,319 - 0,375 343,994 323,978 Particles CO2 Table 12-4: Reduction factor kf (Y) for pollutants (basic year 1990 = 1) for year 1995, 2000, 2005 and 2010 for petrol and diesel cars [FSGV 1997] kf (Y) Car - petrol Car - diesel Pollutant 1995 2000 2005 2010 1995 2000 2005 2010 CO 0,60 0,33 0,23 0,20 0,72 0,51 0,44 0,42 HC + NOx 0,57 0,26 0,15 0,10 0,85 0,67 0,55 0,50 SO2 0,96 0,91 0,87 0,83 0,95 0,26 0,25 0,25 Particles - - - - 0,76 0,51 0,35 0,25 The emission loads are calculated due to the approach described above for the actual state on the basis of the data of the RP survey considering car (driver) and motorcycle trips using the actual speed of each surveyed trip. A comparison with the emission loads out of the 171 emission register in Graz [Forschungsgesellschaft fuer Verbrennungskraftmaschinen und Thermodynamik mbH 2004] showed that the calculations are fitting very well. Due to the fact that the values of the emission registers are calculated much more in detail, these values were taken as reference data for the actual state 2003. In the three scenarios the emissions caused by car/motorcycle trips are estimated considering the percentage change of those trips according to the shift to CLEVER related to the calculated emissions in the actual state. The emissions caused by CLEVER trips (calculation of the CLEVER emissions is described subsequently) are added and the percentage change of the total emissions compared to the (calculated) actual state is viewed. This calculated percentage change of emissions is taken to estimate the emission loads in the three scenarios related to the reference data from the literature. CLEVER Natural gas vehicles emit far less pollutants than petrol or diesel-powered vehicles. It is a lead-free fuel that contains no SO2 and no particulates. Specific emission reductions depend on different factors (e.g. type and make of vehicle) but on the average, anticipated reductions of regulated emissions are as listed in Table 12-5 [European Natural Gas Association 2005]. Table 12-5: Emissions of CNG vehicles compared to petrol [European Natural Gas Association 2005] and diesel cars (FGW 2005] Emissions of CNG vehicles compared to petrol and diesel cars Pollutants Petrol Diesel CO – 76% to – 95% – 50% HC – 85% to – 90% – 90% NOx – 77% – 90% SO2 – 100% – 100% – – 100% – 25% – 10% Particles CO2 Estimating the emissions of CLEVER, which is run by a CNG-engine, the following assumptions are made: Relevant for the estimations are the reductions of a CNG engine compared to a petrol engine, as CNG vehicles have to cope with petrol legislation and the CLEVER engine is an original petrol engine converted to a CNG engine. CO, HC and NOx emissions for CLEVER trips are calculated according to the same formula as presented for petrol and diesel cars, considering an average speed lower than 20 km/h (as the CLEVER is mainly used in urban areas) and therefore taking the cs emission factor, which is in a next step reduced according to the reduction percentages for petrol in Table 12-5. The emission loads per day caused by CLEVER trips in the scenarios result from the multiplication of these estimated values with the kilometres travelled per day in Graz (Table 12-1). For the calculation of the CO2 emissions a mean value of 60 g CO2/km [VENTURI S. 2003] is applied and as well multiplied with the kilometres travelled per day by CLEVER which results in the CO2 emission loads per day in the scenarios. 172 12.1.2 Figures and Costs The emission and CO2 loads decline continuously in the case study city in all three scenarios. That is caused on the one hand by the shift from car (drivers) to the lower emitting CLEVER in all scenarios and in Scenario C additionally by the shift from car (drivers) to environmentally friendly modes like PT and bicycle. The decrease of emission loads ranges between -2,3% and -5,0%, the decline of CO2 between -2,1% and -2,9% according to the scenarios (Table 12-6). Table 12-6: Results of the emission loads and CO2 in GRAZ in [tons/year] in the actual state [Forschungsgesellschaft fuer Verbrennungskraftmaschinen und Thermodynamik mbH 2004] and in the three scenarios, 2003 Emission loads in GRAZ [tons/year] Pollutants Actual state 2003 Relative Relative difference A difference B Scenario A Scenario B Scenario C to the actual to the actual state [%] state [%] Relative difference C to the actual state [%] CO 1.380 t 1.339 t – 2,97% 1.332 t – 3,48% 1.325 t – 3,99% HC 144 t 140 t – 2,78% 139 t – 3,48% 138 t – 4,17% NOx 656 t 639 t – 2,59% 637 t – 2,90% 633 t – 3,51% SO2 20 t 19 t – 5,00% 19 t – 5,00% 19 t – 5,00% Particles 43 t 42 t – 2,33% 41 t – 4,65% 41 t – 4,65% 231.430 t 226.471 t – 2,14% 225.890 t – 2,39% 224.760 t – 2,88% CO2 The costs for hazardous air pollutants (direct emissions emerging directly at the vehicle) include effects on human health (e.g. costs for medical attendance), damage to buildings and harm to vegetation and are calculated according to PISCHINGER et al. [1997] for Austria. The costs for the global effective CO2 are set with 100,70 € per ton according to the recommendations for economic feasibility studies for roads [FGSV 1997] (Table 12-7). Table 12-7: Costs [€] per ton hazardous air pollutants [PISCHINGER R., G. SAMMER, F. SCHNEIDER et al. 1998] and per ton CO2 [FGSV 1997] in Austria, 2003 Costs in € per ton emissions CO HC NOx SO2 Particles CO2 23,93 11.962,52 2.392,71 5.287,86 2.487,93 100,70 Due to decreasing emission and CO2 loads expenses can be saved in all three scenarios (Table 12-8). 173 Table 12-8: Results of the emission and CO2 costs in GRAZ in [M €/year] in the actual state and in the three scenarios, 2003 Emission costs in GRAZ [M €/year] Pollutants Actual state 2003 Scenario A Scenario B Scenario C CO 0,03 M € 0,03 M € 0,03 M € 0,03 M € HC 1,72 M € 1,67 M € 1,66 M € 1,66 M € NOx 1,57 M € 1,53 M € 1,52 M € 1,52 M € SO2 0,11 M € 0,10 M € 0,10 M € 0,10 M € Particles 0,11 M € 0,10 M € 0,10 M € 0,10 M € Sum 3,54 M € 3,43 M € 3,42 M € 3,41 M € – 0,11 M € – 0,12 M € – 0,13 M € – 2,76% – 3,26% – 3,73% 22,81 M € 22,75 M € 22,63 M € – 0,50 M € – 0,56 M € – 0,68 M € – 2,14% – 2,39% – 2,88% Absolute difference to the actual state [M €/year] Relative difference to the actual state [%] CO2 23,31 M € Absolute difference to the actual state [M €/year] Relative difference to the actual state [%] 12.2 Fuel consumption and running costs 12.2.1 Basics for the calculation Fuel consumption is regarded for trips made by car drivers, motorcyclists and CLEVER drivers. For car and motorcycle (respectively petrol and diesel) trips fuel consumption is calculated equivalent to the emissions: FCVG, f (V ) = (c0 + c1 *V 2 + c2 ) V for V > 20 km/h c ⎫ ⎧ FCVG , f (V ) = min ⎨cs , (c 0 + c1 *V 2 + 2 )⎬ V ⎭ ⎩ FCVG , f (V ) for V ≤ 20 km/h Fuel consumption (petrol, diesel – f) of the vehicle group VG related to speed V [g/(km*vehicle)] Table 12-9 gives the parameters c0, c1, c2 and cs of the factors of fuel consumption for petrol and diesel cars. The reduction factors are listed in Table 12-10. 174 Table 12-9: Fuel Parameters c0, c1, c2 of the factors of fuel consumption (related to speed) and factor of fuel consumption cs [g/vehicle-km] for congestion within built-up areas for petrol and diesel cars, 1990 [FSGV 1997] c0 c1 c2 cs Car - petrol 17,7766 0,0023606 1.461,87 174,357 Car - diesel 18,9647 0,0020625 1.139,17 102,85 Table 12-10: kf(Y) Reduction factor kf (Y) for fuel consumption (basic year 1990 = 1) for year 1995, 2000, 2005 and 2010 for petrol and diesel cars [FSGV 1997] 1995 2000 2005 2010 Car - petrol 0,96 0,91 0,87 0,83 Car - diesel 0,95 0,91 0,86 0,85 Contrary to the calculation of the emissions, for the calculation of fuel consumption no reference data from literature are reverted to. The calculated values in the actual state based on the data of the RP survey are considered for estimating the changes and fuel consumption in the three scenarios. For the calculation of CNG consumption by CLEVER 22 g CNG/km [VENTURI S. 2003] are applied and multiplied with the kilometres travelled per day with CLEVER in the scenarios (Table 12-1). For all calculations the actual speed per trip was used, calculated out of the relevant data surveyed. 12.2.2 Figures and Costs Due to the mode shift from car (driver) to CLEVER in the scenarios (compare chapter 9.2 and 9.3) and the resulting decreasing traffic volume (Table 12-1) fuel consumption of petrol and diesel declines in the case study city Graz (Table 12-11). Equivalent to that, CNG consumption increases, with the maximum CNG consumption is registered in Scenario B. As there are no reference data available for CNG-consumption in the year 2003 for Graz, the relative difference to the actual state is not listed in the table – the increase of CNG consumption is only highlighted. In the actual state (2003) in Austria the number of CNG vehicles added up to 250 vehicles [BGW 2003], most of them being busses or taxis. Table 12-11: Fuel consumption in [M l/year] for petrol and diesel and in [t/year] for CNG in GRAZ in the actual state and in the three scenarios, 2003 Fuel consumption (petrol, diesel, CNG) per year in GRAZ Fuel Actual state 2003 Relative Relative Relative difference A difference B difference C Scenario A Scenario B Scenario C to the actual to the actual to the actual state [%] state [%] state [%] Petrol [M l/year] 47,22 M l 45,80 M l – 3,01% 45,51 M l – 3,62% 45,30 M l – 4,07% Diesel [M l/year] 20,42 M l 19,81 M l – 2,99% 19,68 M l – 3,62% 19,59 M l – 4,06% 587,60 t + 825,60 t + 803,10 t + CNG [t/year] no reference 175 The running costs for passenger cars contain fuel costs and basic values for running costs. According to FGSV [1997], the basic values for running costs include amortisation of vehicles, maintenance and servicing, wear of tyres and oil consumption. They are given with 0,09 €/vehicle-km for passenger cars in Austria. As CLEVER is comparable to a conventional car regarding servicing, maintenance etc. and due to the lack of comparable values, its basic values for running costs are assumed to be the same as for passenger cars, again 0,09 €/vehicle-km. As purchase costs for cars are not explicitly included in these values, the purchase costs for CLEVER have also been neglected in this calculation. The total basic values for running costs are calculated on the basis of the traffic volume in the scenarios. For the calculation of fuel costs the net prices (without any taxes) of the average fuel prices of the year 2003 for petrol, diesel and CNG in Austria are taken (Table 12-12). Table 12-12: Fuel Average fuel prices for petrol and diesel [€/l] [OEAMTC 2005] and for CNG [€/kg] [FGW 2005)] in AUSTRIA in 2003 Gross price (incl. taxes) Petroleum tax Tax for natural gas Purchase tax (20%) Net price Petrol [€/l] 0,850 0,41 - 0,15 0,290 Diesel [€/l] 0,740 0,28 - 0,14 0,320 CNG [€/kg] 0,700 - 0,084* 0,12 0,499 * The tax for natural gas is in Austria subject to act of tax for natural gas BGBl. Nr. 201/1996 last modified by BGBl. I Nr. 3 3 71/2003 0,066 €/m [Oesterreichisches Bundesrecht 2003]. The conversion from m CNG into kg is done via the specific density of CNG, which is 0,784 kg [VEST Energie Marketing 2005]. The calculated fuel costs in Scenario C consider the assumed rise of fuel costs to average 112,5%. As described in chapter 7.2.4.1, the sample of the SP survey was originally divided in two classes, where the travel costs in the first class were calculated with fuel prices plus 75%, in the second class with fuel prices plus 150% for Scenario C. The calculation of the CBA for Scenario C is based on this mean value. The results of the components fuel costs (Table 12-13) and basic values for running costs (Table 12-14) are listed in the tables below. The results of the running costs in the actual state and in the three scenarios with the rate of change are presented in Table 12-15. Fuel costs decrease in all three scenarios (despite the additional trips by individual motorised modes, which result from the shift from PT and car passengers to CLEVER) since CNG is more economic – the energy consumption is less than 2,4 l gasoline equivalent. However, the basic values for running costs increase because of the fact that these values are the same for passenger cars and CLEVER and the shift to CLEVER not only comes from car drivers but also from other modes (PT, car passenger), where these components have yet been irrelevant. As the basic values for running costs exceed the fuel costs, total running costs increase as a result. 176 Table 12-13: Fuel costs in [M €/year] for petrol, diesel and CNG in GRAZ in the actual state and in the three scenarios, 2003 Energy costs (petrol, diesel, CNG) in [M €/year] in GRAZ Fuel Actual state 2003 Scenario A Scenario B Scenario C Petrol 13,69 M € 13,28 M € 13,20 M € 13,14 M € Diesel 6,53 M € 6,34 M € 6,30 M € 6,27 M € CNG – 0,29 M € 0,41 M € 0,40 M € Sum 20,22 M € 19,91 M € 19,91 M € 19,81 M € Absolute difference to the actual state [M €/year] – 0,31 M € – 0,31 M € – 0,41 M € – 1,56% – 1,58% – 2,09% Relative difference to the actual state [%] Table 12-14: Basic values for running costs for passenger cars and for CLEVER in [M €/year] in GRAZ in the actual state and in the three scenarios, 2003 Basic values for running costs [M €/year] for passenger cars and for CLEVER in GRAZ Vehicles Actual state 2003 Scenario A Scenario B Scenario C 85,32 M € 84,05 M € 83,70 M € 83,52 M € – 2,44 M € 3,42 M € 3,33 M € 85,32 M € 86,49 M € 87,12 M € 86,85 M € Absolute difference to the actual state [M €/year] + 1,17 M € + 1,80 M € + 1,53 M € + 1,37% + 2,11% + 1,80% Passenger cars CLEVER Sum Relative difference to the actual state [%] Table 12-15: Total running costs (sum of energy costs and basic values for running costs) for passenger cars and for CLEVER in [M €/year] in GRAZ in the actual state and in the three scenarios, 2003 Total running costs (sum of energy costs and basic values for running costs) [M €/year] for passenger cars and for CLEVER in GRAZ Vehicles Actual state 2003 Scenario A Scenario B Scenario C 105,55 M € 103,67 M € 103,20 M € 102,93 M € – 2,73 M € 3,83 M € 3,73 M € 105,55 M € 106,40 M € 107,03 M € 106,66 M € Absolute difference to the actual state [M €/year] + 0,85 M € + 1,48 M € + 1,11 M € + 0,81% + 1,41% + 1,05% Passenger cars CLEVER Sum Relative difference to the actual state [%] 12.3 Noise 12.3.1 Basics for the calculation The assessment of traffic noise within the boundaries of Graz is carried out considering the traffic noise emissions dependent on the kilometres travelled with passenger cars, motorcycles and CLEVER by the inhabitants of Graz and the persons who are affected by 177 traffic noise. The following approach is used for an approximation [SAMMER G., F. WERNSPERGER 1994]: Pa = 37,5 * (log10 ( pkms ) − log10 ( pkma )) Pa … Change of share of persons who feel disturbed by noise caused by traffic [%] pkms … Car kilometres (including car, moped/motorcycle and CLEVER kilometres) in the scenario [km] pkma … Car kilometres (including car and moped/motorcycle kilometres) in the actual state [km] There is no distinction made between the kilometres travelled by car and those travelled by CLEVER, which would have an influence on the noise emissions. Compared to a gasoline engine noise emission of a CNG engine is quite similar. The CNG engine has smoother combustion, but the higher compression ratio used to increase global engine efficiency (high equivalent octane number) slightly increases the noise level, so that in general there is no advantage of a CNG engine in terms of noise emissions in comparison to a gasoline engine [VENTURI S. 2005]. Kilometres travelled per day in Graz in the actual state and in the three scenarios as input parameters for the approximation presented above can be found in Table 12-1. 12.3.2 Figures and Costs As a reference number of inhabitants of Graz who feel disturbed by traffic noise in the actual state the available quantity of 96.000 inhabitants (these are about 42%) is taken from a survey about the “Satisfaction of living regarding traffic noise” by SAMMER G., WERNSPERGER F. (1992). The approximation of the change of share and number of persons who feel disturbed by noise caused by traffic in Graz brought the results presented in Table 12-16. According to the increase of kilometres travelled with an individual motorised mode – car, motorcycle/moped or CLEVER – the noise emissions as well as the share and number of disturbed persons increase at a minimum in all the three scenarios. Table 12-16: Change of share and number of persons who feel disturbed by noise caused by traffic in GRAZ in the actual state and in the three scenarios, 2003 Noise disturbance Number of persons disturbed by traffic noise in Graz Change of share of persons disturbed by traffic noise [%] Actual state 1992 (2003) Scenario A Scenario B Scenario C 96.000 96.212 96.327 96.278 – + 0,22% + 0,34% + 0,29% The costs caused by traffic noise in Graz are calculated according to PISCHINGER, SAMMER, SCHNEIDER et al. [1998], wherein the noise costs are identified to amount to 1,87% of the gross domestic product. This value includes loss in value of accommodation, costs for change of residents, medical attendance as well as costs for pharmaceuticals and 178 cures. In the year 2002, the traffic noise costs in Austria add up to 3,7 billions Euro [Statistk Austria 2001]. 2,3 Mio. Austrians (28,3%) felt disturbed by noise in their home as a result of a census (Micro census 1998). According to this, noise costs in Austria are determined to be € 1.644,10 (2003) per person who is disturbed by noise. The results of the (traffic) noise costs in Graz are presented in Table 12-17. Table 12-17: (Traffic) Noise costs in [Mio. €/year] in Graz in the actual state and in the three scenarios, 2003 Actual state 1992 (2003) Noise costs Scenario A 157,83 M € (Traffic) Noise costs [M €/year] Absolute difference to the actual state [M € / year] Relative difference to the actual state [%] Scenario B Scenario C 158,18 M € 158,37 M € 158,29 M € + 0,35 M € + 0,54 M € + 0,46 M € + 0,22% + 0,34% + 0,29% 12.4 Road accidents 12.4.1 Basics for the calculation Basis for the calculations of road accidents in the scenarios are the number of injured persons in the actual state. To minimise the random error due to low numbers of casualties in Graz, the average of the number of casualties of five years (1999 - 2003) is considered (Table 12-18). Table 12-18: Number of casualties in road accidents according to modes and severity of injury in GRAZ, average of five years (1999 – 2003) [KfV 2000 to 2004] Number of injured persons Road users involved Killed Seriously injured Slightly injured Undefined injured Car driver 3 25 1.236 1 Car passenger 0 13 358 0 Moped, motorcycle 2 41 278 0 Cyclist 2 42 325 0 Pedestrian 5 50 197 0 Public Transport 0 7 68 0 Truck 0 2 42 0 Others 0 4 34 0 12 184 2.539 1 Sum As the number of casualties per mode is dependent on the kilometres travelled with the respective mode the following probability to be killed respectively to be injured has been defined for the actual state: 179 CAm ,k = CAm Km CAm ,k Probability to be killed respectively to be injured per mode and kilometres travelled per year [Casualties/km] (= Average number of casualties per mode and kilometres travelled per year) CAm Average number of casualties per mode and year [Casualties] Km Kilometres travelled per mode per year [km] Table 12-19 shows the results of the probabilities to be killed and to be injured for Graz for the actual state dependent on the kilometres travelled. It can be seen that moped and motorcycle riders have the highest risk to be killed or injured in an accident, followed by pedestrians and cyclists. Table 12-19: Probability to be killed respectively to be injured (according to the severity of injury) per mode and kilometres travelled per year [Casualties / 100.000 km] for GRAZ in the actual state Average number of casualties per mode and per 100.000 kilometres travelled (= CAm,k) Road users involved Killed Seriously injured Slightly injured Undefined injured Car driver 0,08 0,63 30,73 0,02 Car passenger 0,04 1,49 39,81 0,04 Moped, motorcycle 3,86 87,11 596,46 0,86 Cyclist 0,54 12,71 97,87 0,06 Pedestrian 2,39 24,79 97,86 0,00 Public Transport 0,00 0,43 4,18 0,00 In the next step those probabilities are multiplied with the kilometres travelled per mode in the three scenarios which results in the predicted number of casualties: C m ,s = CAm ,k * K m ,s Cs Number of casualties per mode in the scenarios per year CAm ,k Probability to be killed respectively to be injured per mode and kilometres travelled per year [casualties/km] (= average number of casualties per mode and kilometres travelled per year) K m ,s Kilometres travelled per mode in the scenarios per year [km] 180 Developing CLEVER, a lot of effort has been put into the processing and testing of safety. Due to CLEVER’s minimal size and weight it is nearly impossible to make it safer than a car, but it has been achieved that it is as safe as a small car, which brings advantages for those who change mode from moped/motorcycle or bicycle – those modes that bear a high accident risk – to CLEVER. The number of casualties per CLEVER are therefore calculated with the probability of being killed or injured for a car driver in the actual state (first numerical row in Table 12-18 and Table 12-19) and the kilometres travelled with CLEVER in the scenarios (Table 12-1). The increase of kilometres travelled with individual motorized modes in the scenarios also has an influence on the accident risk for pedestrians, as serious pedestrian accidents primarily occur with car drivers. This factor is considered when calculating the rising number of killed or injured pedestrians at constant pedestrian kilometres with the probability of being killed or injured depending on the car kilometres in the scenarios. 12.4.2 Figures and costs The number of casualties per mode is derived from the probability of getting killed or injured and from the kilometres travelled per mode and per year in the scenarios. Depending on the mode shift in the scenarios (compare chapter 9.2 and 9.3), the number of casualties per mode declines or remains constant, while the number of casualties in accidents with CLEVER or with pedestrians rises due to the increase of kilometres travelled with CLEVER respectively with individual motorized modes (Table 12-20). Table 12-20: Number of casualties per year in GRAZ according to the modes in the actual state and in the three scenarios, 2003 Number of casualties in GRAZ according to the modes Modes Actual state 2003 Scenario A Scenario B Scenario C 1.265 1.246 1.241 1.240 Car passenger 372 349 349 349 Motorcycle rider 321 321 321 272 Cyclist 369 369 369 378 Pedestrian 251 255 257 256 PT passenger 75 75 74 74 Others 83 83 83 83 0 37 51 50 2.736 2.735 2.745 2.702 –1 +9 – 34 – 0,1% + 0,3% – 1,3% Car driver CLEVER driver Sum Absolute difference to the actual state Relative difference to the actual state [%] The total number and percentage change of casualties in Graz (Table 12-21) varies in the three scenarios and strongly depends on the risk respectively probability to get killed or injured, which clearly differs between the particular modes and the severity of injury, and on the kilometres travelled with the respective mode. While in Scenario A the number of 181 casualties slightly decreases (decrease of killed/injured car drivers and car passengers, increase of killed/injured CLEVER drivers and pedestrians), it increases in Scenario B due to the instance that also PT passengers who have a minimal risk to get involved in an accident change to the comparable more risky CLEVER. The re-decrease in Scenario C can be explained with the decline of motorcyclists who bear the highest risk to get killed or injured in an accident. Table 12-21: Number of casualties per year in GRAZ according to the severity of injury in the actual state and in the three scenarios, 2003 Number of casualties in GRAZ according to the severity of injury Severity of injury Actual state 2003 Scenario C 12 12 11 184 185 185 180 2.538 2.536 2.546 2.509 2 2 2 2 2.736 2.735 2.745 2.702 –1 +9 – 34 – 0,1% + 0,3% – 1,3% Seriously injured persons Undefined injured persons Sum Scenario B 12 Killed persons Slightly injured persons Scenario A Absolute difference to the actual state Relative difference to the actual state [%] Road accidents cause costs due to damage to property as well as to persons, which partly have to be borne by the general public. Those costs include, in addition to the rescue expenses and medical costs, loss of production, compensation for pain and suffering, costs for administration, law and police as well as costs for losses of time. The assessment of the road accident costs for Austria is done due to the approach of METELKA [1997] (Table 12-22). Table 12-22: Road accident costs for Austria in [€] according to the severity of injury [METELKA 1997] 2003 Costs for persons killed or injured in road accidents [€] Severity of injury Costs for a killed person 963.961,– Costs for a seriously injured person 52.107,– Costs for a slightly injured person 4.416,– Costs for a person who is injured undefined 47.338,– The calculation of accident costs for Graz is based on the number of casualties according to the severity of injury in the scenarios and the accident costs for Austria. They are presented in Table 12-23. It can be seen that the accident costs rise in Scenario A and B, while they decrease in Scenario C. 182 Table 12-23: Accident costs in [M €/year] in GRAZ according to the severity of injury in the actual state and in the three scenarios, 2003 Accident costs [M €/year] in GRAZ according to the severity of injury Severity of injury Actual state 2003 Killed persons Seriously injured persons Slightly injured persons Undefined injured persons Sum Scenario A Scenario B Scenario C 12,15 M € 12,23 M € 12,29 M € 12,04 M € 9,61 M € 9,62 M € 9,64 M € 9,31 M € 11,21 M € 11,20 M € 11,24 M € 11,08 M € 0,09 M € 0,07 M € 0,08 M € 0,08 M € 33,05 M € 33,12 M € 33,25 M € 32,52 M € + 0,07 M € + 0,20 M € – 0,53 M € + 0,2% + 0,6% – 1,6% Absolute difference to the actual state Relative difference to the actual state [%] 12.5 Travel time 12.5.1 Basics for the calculation Travel time of the particular modes has been calculated from the projected values of the survey (subjective specifications of the travel time by the respondents). The definition of the scenarios (compare chapter 7.2.2) determines time advantages for CLEVER compared to car (driver and passenger) in Scenario B and Scenario C. Motorcyclists have no time advantages compared to CLEVER, but neither disadvantages – they are as fast as CLEVER drivers – whereas PT passengers and cyclists gain time savings using CLEVER in all three scenarios. 12.5.2 Figures and costs The reduction of travel time according to the modes results in Scenario A from the mode shift from public transport or bicycle to CLEVER and in Scenario B from the shift from all modes towards CLEVER, while in Scenario C not only time savings are gained but time losses are accepted to some extent resulting from the shift from a faster mode (e.g. car) to a slower one (e.g. PT or bicycle). As in Scenario A only car drivers and car passengers change to CLEVER and no time advantage is determined for the use of CLEVER, no time savings can be registered for this scenario. In Scenario B, the defined time savings justified by various measures favouring the CLEVER vehicle in the city lead to a travel time reduction of approximately 1%. In Scenario C, time savings are partly compensated due to the change from car or motorcycle to PT or bicycle. The results of travel time and time savings in [h / year] for all three scenarios in Graz are shown in Table 12-24 and Table 12-25. 183 Table 12-24: Travel time in [h/year] in GRAZ according to the modes in the actual state and in the three scenarios, 2003 Travel time in [h/day] in GRAZ according to the modes Modes Actual state 2003 Scenario A Scenario B Scenario C 2.372.125 h 2.325.612 h 2.301.097 h 2.294.160 h Car passenger 383.786 h 372.572 h 372.572 h 372.572 h Public Transport 675.389 h 675.389 h 666.465 h 672.832 h Bicycle 194.575 h 194.575 h 194.575 h 237.331 h Motorcycle 146.014 h 146.014 h 146.014 h 128.393 h – 57.727 h 56.485 h 55.339 h 3.771.889 h 3.771.889 h 3.737.207 h 3.760.627 h Absolute difference to the actual state [h/day] 0h h h Relative difference to the actual state [%] 0% – 0,92% – 0,30% Car driver CLEVER Sum Table 12-25: Saving of travel time according to the mode shift in [h/year] in GRAZ in the scenarios compared to the actual state, 2003 Saving of travel time according to the mode shift in [h/day] in GRAZ in the scenarios compared to the actual state Mode shift Scenario A Scenario B Scenario C CLEVER instead of car driver 0h – 28.411 h – 27.648 h CLEVER instead of car passenger 0h – 4.485 h – 4.485 h CLEVER instead of PT 0h – 1.785 h – 1.785 h PT instead of car driver – – + 2.310 h Bicycle instead of car driver – – – 1.106 h Bicycle instead of motorcycle – – + 21.452 h 0h – 34.682 h – 11.262 h Sum Time spent for travelling is a crucial variable in the process of mode choice. It is often used as an argument for the choice of an individual motorised mode (car, moped/motorcycle or CLEVER) instead of travelling by public transport or with another environmentally friendly mode. However, the monetary quantification of travel time is quite difficult, since the valuation of time varies depending on mode, trip purpose and time horizon according to different subjective and objective criteria. For the calculation of time costs, the valuation approach of PISCHINGER et al. [1998] is used, which considers an average hourly rate of 3,45 €/hour. Based on the calculation of travel time in the scenarios, the economic valuation of travel time has been carried out and is presented in Table 12-26. The use of CLEVER and accordingly the mode shift result in cost savings in Scenario B and C due to time savings. 184 Table 12-26: Travel time costs in [M €/year] in GRAZ according to the modes in the actual state and in the three scenarios, 2003 Travel time costs in [M €/year] in GRAZ according to the modes Modes Actual state 2003 Scenario A Scenario B Scenario C Car driver 8,19 M € 8,03 M € 7,94 M € 7,92 M € Car passenger 1,33 M € 1,29 M € 1,29 M € 1,29 M € Public Transport 2,33 M € 2,33 M € 2,30 M € 2,32 M € Bicycle 0,67 M € 0,67 M € 0,67 M € 0,82 M € Motorcycle 0,50 M € 0,50 M € 0,50 M € 0,44 M € – 0,20 M € 0,20 M € 0,19 M € 13,02 M € 13,02 M € 12,90 M € 12,98 M € 0M€ – 0,12 M € – 0,04 M € 0% – 0,92% – 0,30% CLEVER Sum Absolute difference to the actual state [M €/year] Relative difference to the actual state [%] 12.6 Parking infrastructure 12.6.1 Basics for the calculation The estimation of required parking infrastructure for CLEVER vehicles is based on reference data of parking infrastructure in Graz, whereby only the inner districts (districts 1 – 6, where parking management is implemented) are considered. In the other districts there is no need of designated CLEVER parking spaces due to the fact that there is enough parking space available. Table 12-27 gives the actual number of on-street and off-street parking spaces in the inner city of Graz. Table 12-27: Parking infrastructure in the inner city of GRAZ (districts 1 – 6) [GRAZ 2005] Number of parking spaces in the inner city of GRAZ Districts 1 – 6 On-street parking spaces 18.653 Off-street parking spaces 5.129 Total number of parking spaces 23.782 For the estimation of the required number of parking spaces for CLEVER the following data and considerations are taken into account: – Actual number of parking spaces in the districts 1 – 6 in Graz. – Number of car (driver) trips per day to district 1 – 6 done by inhabitants and noninhabitants of Graz in the actual state. – Number of CLEVER trips per day shifted from car (driver) trips, to the defined districts in the three scenarios. – Number of CLEVER trips per day shifted from moped/motorcycle, PT, bicycle, car passenger trips, to the defined districts in the three scenarios. 185 It is assumed that for all CLEVER trips to the relevant districts shifted from car (driver) trips an existing car parking space is replaced by a CLEVER parking space (based on the actual number of parking spaces). Trips shifted from other modes to CLEVER require an additional parking space, as they claimed no parking space before. The number of CLEVER parking spaces corresponds with the number of car parking spaces in a ratio of 2:3 considering the required space of 10 m² for a CLEVER and 15 m² for a car parking space. 12.6.2 Figures and costs An overview of the number of required CLEVER parking spaces in the districts 1 – 6 in Graz according to the three scenarios is given in Table 12-28. The numbers in brackets refer to the corresponding number of car parking spaces due to the needed space. CLEVER parking spaces make up between 1% and 2% of the sum of car parking spaces in the three scenarios. Table 12-28: Required CLEVER parking spaces in the districts 1 – 6 in GRAZ according to the three scenarios, 2003 Required CLEVER parking spaces in the districts 1 – 6 in GRAZ Number of CLEVER parking spaces (corresponding to number of car parking spaces) Scenario A CLEVER on-street parking spaces 213 (142) 577 (385) 556 (371) 58 (39) 159 (159) 153 (102) 271 (181) 736 (490) 709 (472) 0,76% 2,06% 1,99% CLEVER off-street parking spaces - in garages Sum of needed CLEVER parking spaces Share of CLEVER parking spaces (corresponding to car parking spaces) in number of existing car parking spaces Scenario B Scenario C Due to the shift from car (drivers) to CLEVER the demand of car parking spaces decreases in all three scenarios between 1,1% and 1,5% (Table 12-29). The number of required car and CLEVER parking spaces (in the corresponding number of car parking spaces) have been summed up and compared to the existing number of car parking spaces. As a result in Scenario A less car parking spaces and less space is needed than in the actual state. That means that there is still space and vacant parking spaces available despite a designation of car parking in CLEVER parking areas. Contrary, in Scenario B and Scenario C additional parking spaces are needed. But as space on-street as well as off-street is limited and the situation for car parking should not be impaired it is worth to have a look at the degree of utilisation of the existing car parking spaces. That means that at an original degree of utilisation of 99,3% in Scenario B and of 99,5% in Scenario C, the required CLEVER parking spaces can be designated without any restraints for the car drivers. 186 Table 12-29: Required car parking spaces in the districts 1 – 6 in GRAZ in the three scenarios, 2003 Required car parking spaces in the districts 1 – 6 in GRAZ Scenario A Number of required car parking spaces (without CLEVER) Relative difference to the actual state [%] Number of parking spaces-new (including car and CLEVER parking spaces – considering space) Scenario B Scenario C 23.511 23.457 23.429 – 1,14% – 1,37% – 1,48% 23.692 23.947 23.902 – 90 + 165 + 120 Absolute difference to the actual number of car parking spaces For the valuation of a CLEVER parking space only the costs for marking a parking space are taken per year. It is assumed that the marking has to be renewed yearly. As the required marking for a 10 m² sized CLEVER parking space is estimated to be 10,5 running meters and the costs per running meter marking are 7,00 €/rm [Information by MA 46, Verkehrsorganisation und technische Angelegenheiten, Wien 2005] the sum of 73,50 € results for one CLEVER parking space. Based on the estimation of the number of required parking spaces for CLEVER in Graz in the three scenarios the costs for CLEVER parking infrastructure are listed in Table 12-30. Table 12-30: Costs for CLEVER parking infrastructure in [€/year] in GRAZ in the three scenarios, 2003 Costs for CLEVER parking infrastructure [€/year] Scenario A Scenario B Scenario C 19.934 € 54.075 € 52.081 € 12.7 Welfare Losses Welfare losses (or top down costs) mean a reduction of welfare caused by reducing traffic volume as a consequence of increasing travel costs. This is hypothetical relevant only for Scenario C, in which fuel prices increase. As in Graz no trip was skipped due to the increasing fuel prices no welfare losses have been calculated. 12.8 Summary of the Cost Benefit Analysis The CBA aims at evaluating the benefits for the environment and for urban traffic caused by the use of CLEVER based on the hypothetical change of the modal split in the surveyed scenarios. The calculation of the CBA is based on the weighted and projected data of the survey in Graz considering the travel in Graz and the amount of travel of the inhabitants of Graz respectively. The mode shift in the three scenarios and the resulting change in kilometres travelled by individual motorised modes (car driver, moped/motorcycle, CLEVER) are the basis for the 187 evaluation of the benefits for urban traffic comprising CO2 emissions and emissions of hazardous air pollutants, fuel consumption and running costs, noise, road accidents, journey time and parking infrastructure. In all three scenarios an increase of the total costs appears, with the highest rise located in Scenario B (Table 12-31). This, after all not surprising, result is caused due to the fact that not only car drivers substitute their trips by CLEVER trips but that also users of public transport in the actual state decide to shift to CLEVER in the scenarios due to individual advantages of costs and time they gain. That leads to an increase of kilometres travelled by individual motorised modes and thus to a slight increase of noise, road accidents and running costs. A total reduction of all surveyed issues can just be gained if only trips originally done with a motorised vehicle (car drivers or motorcyclists) are substituted by CLEVER. Having a look at the details of the analysis (Table 12-32), the most favourable result is that the loads and thus the costs of CO2 emissions and emissions of hazardous air pollutants can be reduced due to the use of CLEVER. Another positive component in terms of cost reduction and environmentally friendliness is the reduction of fuel consumption (diesel and petrol), which in fact is not apparent in this table as the fuel costs are part of the running costs (compare chapter 12.2). Costs for journey time can be reduced in scenario B and C and accident costs at least in Scenario C. Although positive conclusions can be drawn considering the environmental sub-issue, as benefits can be gained for the environment by the use of CLEVER, it has to be seen that new vehicle concepts like CLEVER with very low CO2 emissions could reduce total emissions caused by transport only on a very limited scale. The number of trips or the mileage travelled with these vehicles (substituting above all car driver trips and not PT trips) has to be very high in order to achieve a high reduction of emissions and to gain far more positive effects for people and the environment. Table 12-31: Results of the Cost Benefit Analysis in GRAZ according to the indicators, costs in [M €/year] in the actual state and in the three scenarios, 2003 GRAZ – Results of the CBA [M €/year] Indicators Actual state 2003 Scenario A Scenario B Scenario C Hazardous air pollutants 3,54 M € 3,44 M € 3,42 M € 3,41 M € CO2-emissions 23,31 M € 22,81 M € 22,75 M € 22,63 M € Running costs 105,55 M € 106,40 M € 107,03 M € 106,66 M € Noise 157,83 M € 158,18 M € 158,37 M € 158,29 M € 33,05 M € 33,12 M € 33,25 M € 32,52 M € Parking infrastructure 0M€ 0,02 M € 0,05 M € 0,05 M € Journey time 13,02 M € 13,02 M € 12,90 M € 12,98 M € - - - - 336,29 M € 336,99 M € 337,78 M € 336,54 M € + 0,69 M € + 1,49 M € + 0,25 M € + 0,21% + 0,44% + 0,07% Road accidents Welfare losses Sum Absolute difference to the actual state [M €/year] Relative difference to the actual state [%] 188 Table 12-32: Cost difference in GRAZ in the three scenarios according to the indicators compared to the actual state 2003 in [M € / year] GRAZ – Cost difference compared to the actual state 2003 [M €/year] Indicators Scenario A Scenario B Scenario C Hazardous air pollutants – 0,10 M € – 0,12 M € – 0,13 M € CO2-emissions – 0,50 M € – 0,56 M € – 0,67 M € Running costs + 0,85 M € + 1,48 M € + 1,11 M € Noise + 0,35 M € + 0,54 M € + 0,46 M € Road accidents + 0,07 M € + 0,20 M € – 0,53 M € Parking infrastructure + 0,02 M € + 0,05 M € + 0,05 M € 0M€ – 0,12 M € – 0,04 M € + 0,69 M € + 1,49 M € + 0,25 M € + 0,21% + 0,44% + 0,07% Journey time Sum Relative difference [%] 189 13 Suggestions for improvements “Our best econometric choice models explain only a fraction of the variation that is found in actual choice behaviour [BRADLEY M. (2004)].” Accordingly, it is reasonable to detect and suggest improvements and modifications of the research. A critical view has to be taken on two issues – the methodical approach, in particular on the SP survey itself, as well as on the process of modelling. When designing and conducting an SP survey, the crucial question is how to gain realistic responses to hypothetical questions concerning future behaviour. To meet this demand, the SP survey has been based on realistic choice sets – each particular trip of the respondents, specified in the screening phase (RP), has been reconsidered under the terms of the different scenarios. Consequently, the differences of the decisive attributes of alternatives (travel costs and travel time) have been rather narrow and did not follow orthogonality. This fact might have hindered a clear mode choice of the respondents in view of the theory of utility maximization as a decision rule. More distinctive values either resulting from more extreme scenarios or from a merge of the single trips over one day in consideration of orthogonality could possibly provide better results. According to D’ ARCIER B. F. (2010) “some additional emphasis on gaining an understanding of the choice processes of individuals, and not just the choices, seems essential … when individuals are going to be confronted by a new situation, for which it is difficult to know beforehand which factors will actually influence individual behavior … or when the models obtained from stated or revealed preference surveys do not provide a statistically satisfactory representation of behaviors.” He argues that an analysis of the entire activity pattern of the respondents, which is defined as the temporal and spatial organisation of activities and trips under constraints would reveal additional arguments for travel behavior beyond the obviously tested attributes travel costs and travel time. “It has thus been observed that the choice of a mode of transport when leaving home in the morning can be because of an activity that takes place in the afternoon and not the result of a trade-off between the respective performances of the car and of public transport on the home-to-work trip [D’ ARCIER B. F. (2010)].” Beside the activity or trip based approach of the SP survey, it would have been reasonable to design and model the purchase decision itself, where the respondents derive utility from each alternative, in this case car and CLEVER. “This utility depends on the characteristics of the alternative; in particular, the consumer places some value on each of the characteristics of the alternative. … The household chooses the alternative that provides it higher utility than any of the other alternatives [TRAIN K. (1993)].” The key characteristics of the alternatives could have been detected in a preliminary focus group, which generally would have been useful to gather qualitive information for the SP design as well as to ease the selection of variables for modelling. LOUVIERE J. J., HENSHER D. A., SWAIT J. D. (2003) emphasise this step for the subsequent phases of the SP study to “learn how consumers think about the decision process, how they gather information about products, when they make decisions, etc.” Moreover, the process of decision making in a hypothetical situation seems to be strongly linked to the sequence of questions in the SP survey and the supply of information about the new conditions – in this case about the new vehicle. An influence on the decision process seems to be possible. It is supposed that if the question about availability of CLEVER, implicating the purchase decision, had been posed before the question about the actual mode choice under hypothetical assumptions, the percentage of CLEVER choice would have 191 corresponded to the results of the corrected mode choice or would have been even lower. Additionally, it would have been useful to explicitly give the purchase costs of CLEVER at each choice situation. Thus providing adequate information at the right moment and raising the required awareness are of crucial importance. “The need for long-term forecasts tends to limit our models to a few basic population variables such as household size, employment, income and age, for which we can obtain or generate some long-term forecasts. This paradigm has effectively prevented the introduction of any additional variables related to attitudes, perceptions, information, constraints, habits, or past histories, all of which could be useful in guiding policies [BRADLEY M. (2004)].” Accordingly specific attitudinal questions – respondents’ attitudes towards traffic related issues concerning congestion, emissions, noise, safety etc. [BATEMAN I. J. (2002)] as well as questions about their social conscience – could have been added to the questionnaire as a valueable input for explaining travel behavior. A critical view on the questionnaire should detect dispensable questions guaranteeing the focus on the SP part of the survey and raising the efficiency of the interview procedure. The point is to avoid the proven methodological influence by shortening the interview and maybe by an evenly distributed number of interviews per interviewer. However, the survey method is not the only issue for improvements. The process of modelling provides additional working points. As the focus of modelling is on improving the explanatory power of the models by adding the most promising variables, one runs the risk of losing sight of the essentials. Pursuing the theory of utility maximisation, the decisive attributes travel costs and travel time are in the center of interest. Nevertheless, the results of the models showed that they did not play the hypothesised role in the choice process. Especially travel costs turned out to have no significant influence on mode choice and could have just as well be omitted. It is thinkable to replace this variable by an additional one representing the respondents’ willingness to buy CLEVER. However, travel time is a significant attribute, which could be emphasised by increasing the difference between travel time of CLEVER and car in the SP survey. In this regard one should be aware of the particular importance of the alternative specific constants, which in fact explain the unconscious feelings of the respondents and the unobserved values. To limit the high value of the alternative specific constants, as a result of the modelling, the unobserved influences have to be put into measureable variables as far as possible. This step should naturally be done in the design process as it is in fact impossible to elicit this kind of information out of the available data. To face the problem of the original/hypothetical and the corrected sample from a different approach one might estimate a nested logit model combining the two decision paths of, in the first place, buying and subsequently using CLEVER. Generally, a deliberate selection of variables in the stage of survey design as well as in the process of modelling is crucial for the explanatory power of the models and finally for the usability of the results. 192 14 Summary Problem Increasing CO2 and exhaust gas emissions, noise as well as consumption of energy and urban space are the most considerable problems in cities arising from the growth of car traffic. Beside the attempt to shift car drivers from driving to environmentally friendly modes like public transport, bicycle or car sharing, it could be a promising policy to encourage them to drive eco friendly cars instead for the sake of keeping their individual (motorised) mobility. As nearly all of the notable automobile manufacturers have eco city cars in their fleet of vehicles, it is up to them to sell them at an acceptable price and to put more effort into the promotion of those cars, of course supported by the political frame. Eco city car The European Commission subdivided the passenger car market on the basis of a number of objective criteria like engine size or length of cars in several segments which could constitute distinct product markets [Commission of the European Communities (1999)]. In fact the length of a car limits its determination as a city car, which naturally should be as small as possible, while the type of engine (electrically powered, CNG, hydrogen fuel cell) indicates its eco friendliness. The eco city car under consideration was conjointly developed within the process of the European project “CLEVER – Compact low emission vehicle for urban transport” by partners of the automotive industry and of three European universities. The outcome of this cooperation was a small, trendy three-wheeled city car (as a prototype) with an exceptional design and various technical features (tilting mechanism, removeable gas cylinder etc.), powered by a CNG engine. Methodology The consequential matter of interest was to what extent this new car appeals to customers and what kind of benefits for the environment result from its use. To pursue these questions a two-stage mobility survey in the Austrian case study city Graz was conducted. In a revealed preference survey the actual daily mobility of the respondents was looked at, followed by a stated preference survey to explore the hypothetical use of the new car under scenario conditions. The outcome was analysed in a descriptive analysis first, demonstrating the potential model shift towards the new car. The impacts on mode choice, in fact the influencing attributes, were analysed in a discrete choice model subsequently. Finally, the benefits for the environment and for urban traffic resulting from its use were estimated in a cost benefit analysis. Stated preference survey “The crucial aspect of ‘Stated Preference Methods’ (SP) is that they present respondents with hypothetical data on alternatives involving trade-offs between the various attributes of these alternatives. Respondents are then asked to make some ‘response’ which may be generally taken as indicating their preferences [ORTUZAR J. de D., GARRIDO R. A. (1991)].” The SP experiment was designed according to the approach of tailoring stated choice, based on the trip diaries of the RP survey. Each respondent had to reconsider his/her own revealed mode choice for each single trip under three scenario conditions and under the assumption of having the new mode CLEVER available. The modes (two in scenario A and B and four in scenario C) had been specified by the attributes “travel time” and “travel costs”. To keep the 193 experiment as realistic as possible, orthogonality had been neglected, which turned out to be a deficiency in the subsequent process of modelling. Modal Shift In course of the analysis of the modal shift the reliability of the hypothetical answers had been checked critically and as a consequence choices had been corrected to gain a more realistic, in fact severely reduced, CLEVER choice. This step of reduction was verified by a comparison of the two samples (original and corrected choice) in the discrete choice models. The launch of CLEVER in Scenario A without any supporting or restrictive measures resulted in a modal shift towards CLEVER of about 1,4% of all trips in Graz, with trips mainly shifting from car driver trips suppositionally induced by the cost advantage, while a small share came from car passenger trips (Figure 14-1). Car passenger 8,4% 8,7% 0,3% 37,3% 36,1% 19,6% 1,2% Public Transport 1,4% CLEVER Car driver 12,8% Bicycle 20,8% Figure 14-1: 0,9% Moped/motorcycle On foot Modal shift in Scenario A in GRAZ, 2003/2004 The measures favouring the use of CLEVER in Scenario B caused a slight rise of the shift from car driver trips towards CLEVER compared to Scenario A (+ 0,2 percentage points) and brought a small share of public transport passengers (0,8%) to use the new vehicle, which resulted in a CLEVER share of 2,4% of all trips in Graz (Figure 14-2). It was assumed that cost as well as time advantages compared to the originally chosen mode made travellers use CLEVER in this scenario. Car passenger 8,7% 8,4% 0,3% 37,3% 0,8% 35,9% 1,4% 19,6% 18,7% Public Transport 2,4% CLEVER Car driver 12,8% Bicycle 20,8% Figure 14-2: On foot 0,9% Moped/motorcycle Modal shift in Scenario B in GRAZ, 2003/2004 194 In Scenario C, users of private motorised vehicles (car drivers, car passengers and motorcyclists) had more than two alternatives for choice – due to the argument of rising fuel prices – and thus the mode shift was more complex (Figure 14-3): Car driver trips were not only substituted by CLEVER trips, but also by trips made by public transport or by bicycle. The share of CLEVER trips shifting from public transport trips stayed constant compared to Scenario B, which argued for the time advantage as the main reason for the use of CLEVER. The share of CLEVER trips in total was 2,3% for Scenario C. Car passenger 8,7% 8,4% 0,3% 37,3% 35,8% Car driver 0,1% 0,8% 1,3% 0,1% 2,3% CLEVER 0,1% 20,8% Figure 14-3: On foot 19,6% 18,9% Public Transport 12,8% 13,0% Bicycle 0,9% 0,8% Moped/motorcycle Modal shift in Scenario C in GRAZ, 2003/2004 Discrete choice Models For discrete choice SP experiments, the usual method of analysis is to model Pj(i), the probability that individual i will choose mode j, within a logit formulation [WATSON S. M. et al. (1996)]. In a stepwise procedure various types of Logit models were estimated, including attributes of alternatives (travel costs and travel time), socio-demographic characteristics (gender, age, and income), mobility pattern (destinations per egress and time in transit) and attitudes and subjective motives (reasons for mode choice and assessment of CLEVER). Additionally, the methodical influence and the influence of awareness on mode choice were tested. Starting with the model of constants and the model of attributes of alternatives variations were tested entering the selected variables. Whether the additional variables added explanatory power to the model or not was estimated by the likelihood ratio test. A comparison of the models was drawn not only between the results of the three scenarios but also according to the two samples “original choice” and “corrected choice”. The distinction between original and corrected choice samples led to a significant improvement of the likelihood as well as of the adjusted R² for all corrected choices of all scenarios and for both types of models (model of constants and model including attributes of alternatives). That revealed that the results of modelling corrected choices – which implied reconsidering and revising the primary SP choice – were more probable than those of the original choices, justifying the step of correction. Evident for all models was that the attribute travel costs (COST) had no influence on mode choice at all, as it was neither significant nor did its coefficient have the expected negative sign, whereas the coefficient of travel time (TIME) was always significant and negative. This fact could probably be traced back to the lack of orthogonality in survey design. Alternative specific constants (ASC) are not only a special characteristic of discrete choice models [MAIER G., WEISS P. (1990)] but were of particular relevance also in this research. 195 They are dummy variables, reflecting the unspecific utility of an alternative. In all observed models the ASC for car was significant and many times higher than the coefficients of the other entering variables, which indicated the unobserved attractiveness of the mode car and the low or even nonexistent explanatory value of the other variables. Although in most of the cases the added variables enhanced the performance of the models, they were rarely significant (for example the socio-demographic variables gender, age, income). However, a significant, but undesireable influence was found regarding variables indicating a methodological influence. With increasing routine an interviewer had, the choice of car was more likely than the probability to choose CLEVER. This result could be explained by the assumption that the interviewer’s own lively interest for the CLEVER, which s/he got across in his/her first interviews, sagged and influenced the respondent’s choice. The second significant variable was the duration of the interview entering with a negative coefficient. One explanation could be that more information of CLEVER, imparted in a longer time period, led to more acceptance of the new vehicle. On the other hand one could assume that with increasing interview time, the respondent tried to cooperate with the interviewer and therefore rather chose the new vehicle. Finally, the influence of awareness was estimated. The hypothesis was that respondents got aware of the consequences owning or having a CLEVER available only after the SP part of the survey, which meant that the decision to own CLEVER was made after the decision to use it for a trip, leading to a reversal of mode/CLEVER choice. To determine this gap between original and corrected choice the two samples were merged (doubled) and an additional variable indicating this influence of awareness was generated. The results of the discrete choice estimation pursuing the different types of models revealed a marginal rise of the adjusted R², while the likelihood ratio χ² tests indicated a significant improvement of the models including the additional awareness variable. Looking at the detailed parameters a high significance of the new variable (INFO_C) was detected in all cases as well as the expected positive coefficient. That meant that the probability to choose car was more likely than to choose CLEVER with the presence of the awareness variable. This approach verified the influence of awareness on the original mode choice and justified the revision of the original mode choice resulting in the corrected choice sample. Benefits for the environement and for urban traffic The cost-benefit analysis aimed at evaluating the benefits for the environment and for urban traffic caused by the use of CLEVER. It was based on the hypothetical change of the modal split in the surveyed scenarios and the resulting change in kilometres travelled by individual motorised modes (car driver, moped/motorcycle, CLEVER). The analysed components comprised CO2 emissions and emissions of hazardous air pollutants, fuel consumption and running costs, noise, road accidents, journey time and parking infrastructure. Positive effects in terms of decreasing loads and costs could be gained regarding CO2 emisssions (-3%) and emissions of hazardous air pollutants (-3%), fuel consumption (-4%) and travel time (-1%). All the other issues as well as the sum of components showed a negative balance. These results could be traced back to the fact that traffic volume increased in all three scenarios due to the reason that not only car drivers changed to CLEVER but also car passengers and public transport users, thus reducing the positive effect of the use of CLEVER. 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Addison-Wesley Verlag, München Additional Internet Links: http://wapedia.mobi/en/Car_classification (access: November 2010) http://europa.eu.int/eur-lex/ (access: November 2010) http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2007:0551:FIN:EN:PDF (access: November 2010) http://europa.eu/legislation_summaries/transport/bodies_objectives/l24484_en.htm (access: November 2010) http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:140:0001:0015:EN:PDF (access: November 2010) http://www.statistik.at/wed_de/statistiken/verkehr/strasse/kraftfahrzeuge__bestand/index.html (access: September 2010) http://www.mazda.at/aboutmazda/concept_cars/kiyora/ (access November 2010) http://www.conceptcarz.com/vehicle/z15695/Mazda-Kiyora-Concept.aspx (access November 2010)] http://www.conceptcarz.com/vehicle/z17444/Renault-Twizy-ZE-Concept.aspx, November 2010)] 203 (access: 16 Annex 16.1 Questionnaires Q 1a CAR DRIVER Interview No. IIN First name Start of the trip (time): Arrival at the destination (time): Trip No. Duration of the trip: Trip length: min. km Purpose of the trip: 1 Please state for which personal reasons you used the car for this trip: (Please indicate the priority of these reasons. 1 = most important reason etc.) The reasons are the same as for the previous trip. 2 Number of passengers? Who did you take (family member, colleague etc.)? Which type of baggage did you take? Please estimate your total trip costs: 3 € Where did you park your car at the destination? Non-chargeable parking space on public land Short-term parking area Parking fees € Public garage Parking fees € Private parking space Parking fees € How did you get from the parking space to your destination? Mode of transport (on foot, PT, bicycle, car passenger etc.) Please estimate the distance from the parking space to your destination: 204 m 4 Please explain this trip using public transport instead of using the car! I am not aware of it There is no PT supply for this trip Where do you get on (stop at origin)? Which lines do you use? Where do you get off (stop at destination)? Total duration of the trip: 5 min. For which reasons didn’t you use any of the following modes for this trip on the reporting day? Please explain the reasons against your choice of these modes: Car passenger PT Bicycle Is it for you in principle imaginable to use the following modes for this trip? Yes Car passenger No possibly Please explain the resaons respectively circumstances: PT Bicycle 205 CLEVER – Assessment Interview No. 1 IIN Q2 First name How do you assess the CLEVER and its features? very positively +2 very negatively +1 Idea of the CLEVER Design Capacity of transporting persons (2 persons) Capacity of transporting luggage (hand luggage) Maximum speed: 110 km/h Acceleration 0 – 60 km/h in 9 sec. Driving range: 160 km Consumption: 7,5 l CNG/100 km (≅ 4,1 l petrol/100km) Running costs: 0,18 €/km (conventional car: 0,35 €/km) Purchase costs: 9.000 € Tilting mechanism Full automatic transmission No heating Pollutant emissions Other comments: 206 -1 -2 Please give reasons for your assessment: 2 Could you imagine to use the CLEVER? yes possibly no For what reasons what you use the CLEVER? – as driver – as passenger Under which circumstances would you use the CLEVER? – as driver – as passenger For what kind of trips would you use the CLEVER? (Plural entries possible!) Work place Shopping Business trip Private visiting Education/school Active sports Other, please specify: Why won’t you consider to use the CLEVER? (Please indicate the priority of these reasons!) 207 CAR DRIVER Interview No. IIN Q 3a Scenario A First name Trip No. Start time SCENARIO Trip costs: Duration of the trip: Which mode would you use under these (altered) conditions for this trip? Trip length A therefrom fuel costs CAR € € CLEVER € € CAR Min. CLEVER Min. CAR CLEVER Would you change your destination? If yes, to what extent (purpose, destination)? Yes No Would you do any other trips additionally? If yes, which? Yes No Would you start your trip earlier/later? If yes, why? Yes No Start time: Reason: Does the change of your mode choice affect the trips of the other household members? Purpose of the trip Yes If yes, to what extent? Please give reasons for your mode choice: 208 No CAR DRIVER Q 3a Scenario C Interview group Start time SCENARIO Trip costs: Duration of the trip: Would you skip this trip? Which mode would you use under these (altered) conditions for this trip? Trip length Purpose of the trip therefrom fuel costs C CAR € € CLEVER € € PT € Bicycle € CAR Min. CLEVER Min. PT Min. Bicycle Min. On Foot Min. No Yes Car passenger CAR CLEVER PT Bicycle On Foot Would you change your destination? If yes, to what extent (purpose, destination)? Yes No Would you do any other trips additionally? If yes, which? Yes No Would you start your trip earlier/later? If yes, why? Yes No Start time: Reason: Does the change of your mode choice affect the trips of the other household members? Yes If yes, to what extent? Please give reasons for your mode choice: 209 No CLEVER – Use Interview No. IIN Use in scenario Q4 First name A B C Which kind of availability of the CLEVER would you prefer and why? Purchase (€ 9.000,–) Rental (€20,–/day) Reason: Reason: (on a daily or on a weekly basis) Car Sharing Reason: (€1,50/h) Other: Reason: Sonstiges: Which status would the CLEVER have in relation to the existing car(s) in your household? Single motor vehicle (apart from moped or motorcycle) Second vehicle Third vehicle Fourth vehicle Would the CLEVER replace one of your motor vehicles (car, moped, motorcycle)? If yes, which one? Yes Which vehicle? No 210 16.2 Modal split in detail Table 16-1: Modal split related to gender in GRAZ in the actual state and in the three scenarios (grossed up), 2003/2004 Modal split against gender in GRAZ Modal Split in % Actual state A B C Car driver Car passenger CLEVER Public Transport Moped/ motorcycle Bicycle On Foot Sum N (trips) male 47,0% 5,0% 0,0% 15,1% 1,2% 14,5% 17,2% 100% 464.100 female 27,7% 12,3% 0,0% 23,9% 0,5% 11,2% 24,5% 100% 467.600 male 45,6% 5,0% 1,4% 15,1% 1,2% 14,5% 17,2% 100% 464.100 female 26,7% 11,8% 1,4% 23,9% 0,5% 11,2% 24,5% 100% 467.600 male 45,6% 5,0% 1,4% 15,1% 1,2% 14,5% 17,2% 100% 464.100 female 26,3% 11,8% 3,5% 22,3% 0,5% 11,2% 24,5% 100% 467.600 male 45,9% 5,0% 1,2% 15,1% 1,1% 14,6% 17,2% 100% 464.100 female 25,8% 11,8% 3,5% 22,5% 0,5% 11,4% 24,5% 100% 467.600 Table 16-2: Modal split related to age in GRAZ in the actual state and in the three scenarios, (grossed up) 2003/2004 Modal split against age in GRAZ Scenario C Scenario B Scenario A Actual state Modal Split in % Car driver Car passeng er CLEVER Public Transport Moped/ motorcycle Bicycle On Foot Sum N (trips) 16 – 25 years 12,5% 15,4% 0,0% 30,7% 3,0% 14,0% 24,5% 100% 201.900 26 – 45 years 47,5% 5,3% 0,0% 13,8% 0,3% 15,7% 17,3% 100% 373.900 46 – 65 years 45,4% 7,7% 0,0% 16,4% 0,2% 10,0% 20,3% 100% 262.200 > 65 years 27,2% 10,2% 0,0% 27,3% 0,0% 7,0% 28,4% 100% 93.600 16 – 25 years 12,5% 15,4% 0,0% 30,7% 3,0% 14,0% 24,5% 100% 201.900 26 – 45 years 47,2% 4,7% 0,9% 13,8% 0,3% 15,7% 17,3% 100% 373.900 46 – 65 years 43,8% 7,7% 1,6% 16,4% 0,2% 10,0% 20,3% 100% 262.200 > 65 years 21,5% 10,2% 5,7% 27,3% 0,0% 7,0% 28,4% 100% 93.600 16 – 25 years 12,5% 15,4% 0,0% 30,7% 3,0% 14,0% 24,5% 100% 201.900 26 – 45 years 47,2% 4,7% 0,9% 13,8% 0,3% 15,7% 17,3% 100% 373.900 46 – 65 years 43,2% 7,7% 5,1% 13,5% 0,2% 10,0% 20,3% 100% 262.200 > 65 years 21,0% 10,2% 6,2% 27,3% 0,0% 7,0% 28,4% 100% 93.600 16 – 25 years 12,5% 15,4% 0,0% 30,7% 3,0% 14,0% 24,5% 100% 201.900 26 – 45 years 47,2% 4,7% 0,9% 13,8% 0,2% 15,8% 17,3% 100% 373.900 46 – 65 years 43,0% 7,7% 4,9% 13,9% 0,2% 10,0% 20,3% 100% 262.200 > 65 years 20,4% 10,2% 5,7% 27,3% 0,0% 8,1% 28,4% 100% 93.600 Table 16-3: Modal split related to trip purpose in GRAZ in the actual state, (grossed up) 2003/2004 Modal split against trip purpose in GRAZ Car driver Car passenger Business 66,0% 5,6% 0,0% 9,8% 0,2% 9,4% 8,9% 100% 69.800 Commuter 49,2% 3,9% 0,0% 19,5% 0,8% 13,3% 13,4% 100% 215.000 Education 7,1% 10,7% 0,0% 35,2% 2,3% 21,3% 23,4% 100% 134.200 Shopping 31,7% 8,8% 0,0% 19,7% 0,4% 10,8% 28,7% 100% 244.600 Leisure 35,7% 14,4% 0,0% 15,1% 0,8% 12,1% 21,9% 100% 226.900 Bringing 66,9% 0,0% 0,0% 9,1% 0,0% 5,0% 19,0% 100% 41.100 Actual state Modal Split in % CLEVER Public Transport 211 Moped/ motorcycle Bicycle On Foot Sum N (trips) Table 16-4: Modal split related to trip purpose in GRAZ in the three scenarios, (grossed up) 2003/2004 Modal split against trip purpose in GRAZ Car driver Car passenger Business 66,0% 2,5% 3,2% 9,8% 0,2% 9,4% 8,9% 100% 69.800 Commuter 49,2% 3,9% 0,0% 19,5% 0,8% 13,3% 13,4% 100% 215.000 Education 6,3% 10,7% 0,8% 35,2% 2,3% 21,3% 23,4% 100% 134.200 Shopping 29,5% 8,8% 2,2% 19,7% 0,4% 10,8% 28,7% 100% 244.600 Leisure 33,8% 14,4% 1,9% 15,1% 0,8% 12,1% 21,9% 100% 226.900 Bringing 66,9% 0,0% 0,0% 9,1% 0,0% 5,0% 19,0% 100% 41.100 Business 66,0% 2,5% 3,2% 9,8% 0,2% 9,4% 8,9% 100% 69.800 Commuter 48,2% 3,9% 4,5% 16,0% 0,8% 13,3% 13,4% 100% 215.000 Education 6,3% 10,7% 0,8% 35,2% 2,3% 21,3% 23,4% 100% 134.200 Shopping 29,5% 8,8% 2,2% 19,7% 0,4% 10,8% 28,7% 100% 244.600 Leisure 33,8% 14,4% 1,9% 15,1% 0,8% 12,1% 21,9% 100% 226.900 Bringing 66,9% 0,0% 0,0% 9,1% 0,0% 5,0% 19,0% 100% 41.100 Business 66,0% 2,5% 3,2% 9,8% 0,2% 9,4% 8,9% 100% 69.800 Commuter 48,3% 3,9% 4,4% 16,0% 0,6% 13,5% 13,4% 100% 215.000 Education 6,4% 10,7% 0,7% 35,2% 2,3% 21,3% 23,4% 100% 134.200 Shopping 29,2% 8,8% 2,0% 20,1% 0,4% 10,8% 28,7% 100% 244.600 Leisure 33,5% 14,4% 1,7% 15,1% 0,8% 12,6% 21,9% 100% 226.900 Bringing 66,9% 0,0% 0,0% 9,1% 0,0% 5,0% 19,0% 100% 41.100 Scenario C Scenario B Scenario A Modal Split in % Table 16-5: CLEVER Public Transport Moped/ motorcycle Bicycle On Foot Sum N (trips) Modal split related to trip length in GRAZ in the actual state and in scenario A, (grossed up) 2003/2004 Modal split against trip length in GRAZ Scenario A Actual state Modal Split in % Car driver Car passenger CLEVER Public Transport Moped/ motorcycle Bicycle On Foot Sum N (trips) < 1 km 11,1% 2,8% 0,0% 4,8% 0,2% 14,1% 67,0% 100% 216.300 1,1 – 2 km 28,5% 7,5% 0,0% 16,3% 1,0% 25,7% 21,1% 100% 141.200 2,1 – 3 km 37,4% 7,5% 0,0% 25,7% 1,5% 19,6% 8,3% 100% 122.900 3,1 – 5 km 42,0% 10,3% 0,0% 33,4% 1,0% 10,3% 3,3% 100% 172.200 5,1 – 10 km 52,9% 13,2% 0,0% 25,7% 1,1% 5,0% 2,2% 100% 164.700 10,1 – 15 km 64,5% 12,8% 0,0% 17,6% 1,1% 4,1% 0,0% 100% 36.100 15,1 – 20 km 67,1% 9,6% 0,0% 16,4% 0,6% 6,4% 0,0% 100% 17.100 > 20 km 70,9% 15,1% 0,0% 13,4% 0,3% 6,4% 0,0% 100% 61.000 < 1 km 10,6% 2,8% 0,5% 4,8% 0,2% 14,1% 67,0% 100% 216.300 1,1 – 2 km 28,5% 7,5% 0,0% 16,3% 1,0% 25,7% 21,1% 100% 141.200 2,1 – 3 km 35,7% 7,5% 1,7% 25,7% 1,5% 19,6% 8,3% 100% 122.900 3,1 – 5 km 40,1% 10,3% 1,9% 33,4% 1,0% 10,3% 3,1% 100% 172.200 5,1 – 10 km 50,9% 13,2% 2,0% 25,7% 1,1% 5,0% 2,2% 100% 164.700 10,1 – 15 km 61,5% 12,8% 3,0% 17,6% 1,1% 4,1% 0,0% 100% 36.100 15,1 – 20 km 67,1% 9,6% 0,0% 16,4% 0,6% 6,4% 0,0% 100% 17.100 > 20 km 70,9% 11,4% 3,6% 13,4% 0,3% 0,2% 0,0% 100% 61.000 212 Table 16-6: Modal split related to trip length in GRAZ in scenario B and C, (grossed up) 2003/2004 Modal split against trip length in GRAZ Scenario C Scenario B Modal Split in % Car driver Car passenger Public Transport CLEVER Moped/ motorcycle Bicycle On Foot Sum N (trips) < 1 km 10,5% 2,8% 0,5% 4,8% 0,2% 14,1% 67,0% 100% 216.300 1,1 – 2 km 28,5% 7,5% 0,0% 16,3% 1,0% 25,7% 21,1% 100% 141.200 2,1 – 3 km 35,5% 7,5% 1,9% 25,7% 1,5% 19,6% 8,3% 100% 122.900 3,1 – 5 km 39,9% 10,3% 6,4% 29,0% 1,0% 10,3% 3,1% 100% 172.200 5,1 – 10 km 50,0% 13,2% 2,8% 25,7% 1,1% 5,0% 2,2% 100% 164.700 10,1 – 15 km 61,2% 12,8% 3,2% 17,6% 1,1% 4,1% 0,0% 100% 36.100 15,1 – 20 km 67,1% 9,6% 0,0% 16,4% 0,6% 6,4% 0,0% 100% 17.100 > 20 km 70,9% 11,4% 3,6% 13,4% 0,3% 0,2% 0,0% 100% 61.000 < 1 km 10,1% 2,8% 0,5% 4,8% 0,2% 14,6% 67,0% 100% 216.300 1,1 – 2 km 27,7% 7,5% 0,0% 17,0% 1,0% 25,7% 21,1% 100% 141.200 2,1 – 3 km 35,7% 7,5% 1,7% 25,7% 1,5% 19,6% 8,3% 100% 122.900 3,1 – 5 km 40,1% 10,3% 6,3% 29,0% 1,0% 10,3% 3,1% 100% 172.200 5,1 – 10 km 50,3% 13,2% 2,6% 25,7% 0,8% 5,3% 2,2% 100% 164.700 10,1 – 15 km 61,5% 12,8% 3,0% 17,6% 1,1% 4,1% 0,0% 100% 36.100 15,1 – 20 km 67,1% 9,6% 0,0% 16,4% 0,6% 6,4% 0,0% 100% 17.100 > 20 km 70,9% 11,4% 3,6% 13,4% 0,3% 0,2% 0,0% 100% 61.000 Table 16-7: Modal split related to destinations per egress in GRAZ in the actual state and in the three scenarios, (grossed up) 2003/2004 CLEVER Public Transport – 22,3% Moped/ motorcycle Bicycle On Foot Sum N (trips) 1,2% 14,3% 22,6% 100% 560.000 30,3% 9,3% 2 dest. per egress 45,6% 11,7% – 15,1% 0,7% 12,0% 15,0% 100% 188.000 ≥ 3 dest. per egress 50,2% 3,6% – 15;5% 0,0% 9,2% 21,5% 100% 183.000 Scenario A Car passenger 1 dest. per egress 29,1% 9,3% 1,3% 22,3% 1,2% 14,3% 22,6% 100% 560.000 2 dest. per egress 43,5% 10,5% 3,2% 15,2% 0,7% 12,0% 15,0% 100% 188.000 ≥ 3 dest. per egress 50,2% 3,6% 0,0% 15,5% 0,0% 9,2% 21,5% 100% 183.000 Scenario B Car driver 1 dest. per egress 28,7% 9,3% 3,0% 21,0% 1,2% 14,3% 22,6% 100% 560.000 2 dest. per egress 43,5% 10,5% 3,2% 15,2% 0,7% 12,0% 15,0% 100% 188.000 ≥ 3 dest. per egress 50,2% 3,6% 0,0% 15,5% 0,0% 9,2% 21,5% 100% 183.000 Scenario C Modal Split in % 1 dest. per egress 28,5% 9,3% 2,8% 21,1% 1,1% 14,6% 22,6% 100% 560.000 2 dest. per egress 43,5% 10,5% 3,2% 15,3% 0,7% 12,0% 15,0% 100% 188.000 ≥ 3 dest. per egress 50,2% 3,6% 0,0% 15,5% 0,0% 9,2% 21,5% 100% 183.000 Actual state 1 dest. per egress 213 16.3 Results of the correlation Table 16-8: Extract of the results of the correlation (car driver, ABC, unweighted), dependent variable: Mode choice CLEVER (1) Variable Label Name Scale Mean St. Dev. Cases Corr. Sign. Bike PT Car CLEVER b Attributes of alternatives CLEVER_COST CLV_COST metric 2,20 4,27 1.065 -0,081 0,008 CLEVER_LNCOST CLV_LNCOST metric 0,04 1,15 1.065 -0,108 0,000 CLEVER_FUEL COST CLV_FCOST metric 0,45 0,93 1.065 -0,075 0,014 CLEVER_LNFUEL COST CLV_LNFCOST metric -1,59 1,18 1.065 -0,090 0,003 CLEVER_TIME CLV_TIME metric 18,70 18,60 1.065 -0,113 0,000 CLEVER_LNTIME CLV_LNTIME metric 2,62 0,77 1.065 -0,100 0,001 CLEVER_COST X TIME CLV_COXTI metric 99,96 381,40 1.065 -0,081 0,008 CAR_COST CAR_COST metric 4,79 9,31 1.065 -0,081 0,008 CAR_LNCOST CAR_LNCOST metric 0,82 1,14 1.065 -0,108 0,000 CAR_FUEL COST CAR_FCOST metric 1,06 2,17 1.065 -0,077 0,012 CAR_LNFUEL COST CAR_LNFCOST metric -0,74 1,18 1.065 -0,096 0,002 CAR_TIME CAR_TIME metric 22,37 20,93 1.065 -0,101 0,001 CAR_LNTIME CAR_LNTIME metric 2,82 0,74 1.065 -0,085 0,005 CAR_COST X TIME CAR_COXTI metric 260,39 983,74 1.065 -0,080 0,009 CAR – CLEVER Difference of costs COST_DIF metric 2,60 5,07 1.065 -0,080 0,008 PT_COST PT_COST metric 2,32 3,34 326 -0,079 0,153 PT_LNCOST PT_LNCOST metric 0,59 0,53 326 -0,085 0,125 PT_TIME PT_TIME metric 32,60 30,20 326 -0,126 0,022 PT_LNTIME PT_LNTIME metric 3,13 0,92 326 -0,173 0,002 BIKE_COST BI_COST metric 0,56 1,09 355 -0,109 0,039 BIKE_LNCOST BI_LNCOST metric -1,31 1,12 355 -0,123 0,020 BIKE_TIME BI_TIME metric 56,15 109,16 355 -0,109 0,039 BIKE_LNTIME BI_LNTIME metric 3,29 1,12 355 -0,123 0,020 c Trip related attributes Baggage_grouped BAG_group ordinal 0,78 0,61 1.059 -0,232 0,000 Baggage_no_Dummy BAG_no dummy 0,31 0,46 1.059 0,221 0,000 PURPOSE_bringing_Dummy T_BRIN dummy 0,08 0,28 1.065 0,201 0,000 PURPOSE_business_Dummy T_BUSI dummy 0,14 0,35 1.065 -0,166 0,000 TRIP LENGTH (km) T_KM metric 11,21 21,81 1.065 -0,081 0,008 TRIP LNLENGTH T_LNKM metric 1,67 1,14 1.065 -0,113 0,000 TRIP LENGTH_grouped KM_group ordinal 4,30 1,93 1.065 -0,109 0,000 d Socio-demographic characteristics – Household INCOME_LOW_Dummy INC_low dummy 0,26 0,44 963 0,355 0,000 INCOME_grouped INC1_group ordinal 2,10 0,88 963 -0,306 0,000 HOUSEHOLD_KIDS ≤ 6 years (yes=1) HH_kids dummy 0,15 0,36 1.065 0,246 0,000 INCOME_high_Dummy INC_high dummy 0,26 0,44 963 -0,204 0,000 Number of PERSONS ≥ 6 years HH_persons metric 3,16 1,20 1.065 -0,138 0,000 INCOME_medium_Dummy INC_med dummy 0,48 0,50 963 -0,130 0,000 HOUSEHOLD Category_DUMMY (House=1) HH_CAT dummy 0,63 0,48 1.032 0,104 0,001 214 Table 16-9: Extract of the results of the correlation (car driver, ABC, unweighted), dependent variable: Mode choice CLEVER (2) Variable Label Name Scale - - Mean St. Dev. Cases Corr. Sign. - - - 1.053 0,149 0,000 e Availability of transport modes - - - f Socio-demographic characteristics – Person AGE_group AGE_group ordinal 3,70 0,76 AGE P_AGE metric 49,74 13,34 1.053 -0,101 0,001 Age_26 - 45 years_Dummy P_AGE26 dummy 0,34 0,48 1.053 0,263 0,000 Age_46 - 65 years_Dummy P_AGE46 dummy 0,48 0,50 1.053 -0,154 0,000 Age_16 - 25 years_Dummy P_AGE16 dummy 0,05 0,21 1.053 -0,096 0,002 Age_> 65 years_Dummy P_AGE65 dummy 0,13 0,34 1.053 -0,082 0,008 GENDER P_MALE dummy 0,55 0,50 1.065 -0,115 0,000 g Mobility pattern Time in TRANSIT (min) T_TRANSIT metric 106,78 71,98 1.065 -0,160 0,000 Number of destinations per egress T_DES metric 2,19 1,46 1.065 -0,151 0,000 Time at HOME (min) T_HOME metric 915,19 228,31 864 0,121 0,000 REASON_COST advantage T_RECOST dummy 0,19 0,39 1.065 0,668 0,000 REASON_ENVIRONMENT T_REENVIR dummy 0,07 0,25 1.065 0,614 0,000 CLEVER_hypothetic choice_yes_Dummy CLV_yes dummy 0,14 0,35 1.065 0,438 0,000 h Subjective motives REASON_TIME advantage T_RETIME dummy 0,05 0,21 1.065 0,391 0,000 CLEVER_hypothetic choice_no_Dummy CLV_no dummy 0,64 0,48 1.065 -0,385 0,000 REASON_CLEVER_positive comments CLV_REPOS dummy 0,03 0,17 1.065 0,383 0,000 CLEVER_Assessment_Design M_CL_1 ordinal 2,38 0,99 1.059 0,379 0,000 CLEVER_Assessment_ Emissions M_CL_14 ordinal 3,19 1,03 1.065 0,247 0,000 CLEVER_purchase_Dummy CLV_purch dummy 0,43 0,50 282 0,224 0,000 INTERVIEWER_grouped INT_group ordinal 1,68 0,83 1.065 0,389 0,000 INTERVIEW Number per interviewer INT_NO ordinal 11,79 10,21 1.065 -0,294 0,000 INTERVIEW TIME (min) INT_MIN metric 72,63 22,05 1.065 -0,239 0,000 i Method 215 16.4 Results of the discrete choice estimation Table 16-10: Model of constants: Results of the discrete choice estimation depending on scenarios and choice sample (original versus corrected) Scenario Choice Constant b / St. Er. P[│Z│>z] Log lik. R-sqrd. A_CAR 1.65042035 .15089476 10.938 .0000 -143.4805 .36318 corrected A_CAR 3.06419627 .26867380 11.405 .0000 -59.26421 .73696 original A_CAR 1.53900125 .14542745 10.583 .0000 -151.5781 .32724 corrected A_CAR 2.83899380 .24276668 11.694 .0000 -69.46357 .69169 A_CAR 5.78522489 1.19702086 4.833 .0000 A_CLEVER 4.44111282 1.20163302 3.696 .0002 -176.3787 .60826 .36225479 1.55232484 .233 .8155 A_CAR 5.95495317 1.19676636 4.976 .0000 A_CLEVER 3.20541385 1.21673730 2.634 .0084 -85.12873 .81093 .36225479 1.55232484 .233 .8155 B original A_PT C corrected A_PT original A_CAR 1.59368319 .10468946 15.223 .0000 -295.2001 .34490 corrected A_CAR 2.94589720 .18007280 16.359 .0000 -128.9222 .71390 A_CAR 5.81670237 1.19677881 4.860 .0000 A_CLEVER 4.31028720 1.19861864 3.596 .0003 -472.6088 .65030 .36225478 1.55232482 .233 .8155 A_CAR 5.96216392 1.19670780 4.982 .0000 A_CLEVER 3.08533327 1.20428376 2.562 .0104 -214.2685 .84146 .36225478 1.55232482 .233 .8155 AB original A_PT B C Standard Er. original A A Coefficient corrected A_PT Table 16-11: Attributes of alternatives: Scenario A (original mode choice) --> NLOGIT;Lhs=CHOICE;Choices=car,CLEVER;Rhs=ONE,COST;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 4 | | Log likelihood function -143.4633 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .36326 .35965 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .00693072 .03807733 .182 .8556 A_CAR 1.63497199 .17240141 9.484 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0069 COST | 4.485 8.733| 4.710 9.263 | | A_CAR 1.6350 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ 216 | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 254.00000 49.00000 303.00000 CLEVER | 44.00000 8.00000 52.00000 Total | 298.00000 57.00000 355.00000 Table 16-12: Attributes of alternatives: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 5 | | Log likelihood function -59.08211 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .73777 .73629 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 1] = 9.49441 | | Prob [ chi squared > value ] = .00206 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .05173634 .09871622 .524 .6002 A_CAR 2.96301969 .31774511 9.325 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 337.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0517 COST | 4.485 8.733| 4.573 8.931 | | A_CAR 2.9630 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 322.00000 15.00000 337.00000 CLEVER | 17.00000 1.00000 18.00000 Total | 339.00000 16.00000 355.00000 Table 16-13: Attributes of alternatives: Scenario B (original mode choice) --> NLOGIT;Lhs=CHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 5 | | Log likelihood function -149.0380 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .33851 .33288 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 6.69825 | | Prob [ chi squared > value ] = .03511 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .07555944 .05157408 1.465 .1429 TIME -.06605730 .03008935 -2.195 .0281 A_CAR 1.72262688 .19766832 8.715 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 297.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0756 COST | 4.485 8.733| 4.753 9.351 | | TIME -.0661 TIME | 22.372 20.947| 23.189 22.234 | | A_CAR 1.7226 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : 217 | Utility Function | | 58.0 observs. | | Coefficient | All 355.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0756 COST | 2.024 3.954| 1.401 1.868 | | TIME -.0661 TIME | 16.868 17.030| 12.759 7.977 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 244.00000 53.00000 297.00000 CLEVER | 47.00000 11.00000 58.00000 Total | 291.00000 64.00000 355.00000 Table 16-14: Attributes of alternatives: Scenario B (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 7 | | Log likelihood function -64.07668 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .71560 .71318 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 23.38863 | | Prob [ chi squared > value ] = .00001 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .38192747 .19040439 2.006 .0449 TIME -.16041733 .05240815 -3.061 .0022 A_CAR 3.10064393 .33566732 9.237 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 333.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3819 COST | 4.485 8.733| 4.600 8.981 | | TIME -.1604 TIME | 22.372 20.947| 22.604 21.390 | | A_CAR 3.1006 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 355.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3819 COST | 2.024 3.954| 1.235 1.189 | | TIME -.1604 TIME | 16.868 17.030| 11.318 7.344 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 314.00000 19.00000 333.00000 CLEVER | 20.00000 2.00000 22.00000 Total | 334.00000 21.00000 355.00000 Table 16-15: Attributes of alternatives: Scenario C (original mode choice) --> NLOGIT;Lhs=CHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 354 | | Iterations completed 7 | | Log likelihood function -170.4278 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -450.2407 .62147 .61957 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 12.74098 | | Prob [ chi squared > value ] = .00171 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 1 bad obs. | +---------------------------------------------+ 218 +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .19337365 .07573224 2.553 .0107 TIME -.05026118 .02908119 -1.728 .0839 A_CAR 5.23492323 1.20413568 4.347 .0000 A_CLEVER 4.08796941 1.20348659 3.397 .0007 A_PT .21673899 1.55374740 .139 .8891 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 282.0 observs. | | Coefficient | All 354.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1934 COST | 5.409 10.390| 6.045 11.467 | | TIME -.0503 TIME | 22.342 20.969| 23.787 22.750 | | A_CAR 5.2349 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 66.0 observs. | | Coefficient | All 354.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1934 COST | 2.321 4.484| 1.301 1.266 | | TIME -.0503 TIME | 16.839 17.046| 12.152 7.078 | | A_CLEVER 4.0880 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1934 COST | 2.320 3.342| .750 .000 | | TIME -.0503 TIME | 32.560 30.232| 19.500 8.888 | | A_PT .2167 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1934 COST | .282 .213| .050 .000 | | TIME -.0503 TIME | 28.242 21.323| 5.000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 223.00000 58.00000 1.00000 1.00000 282.00000 CLEVER | 51.00000 15.00000 .0000000D+00 .0000000D+00 66.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 278.00000 74.00000 1.00000 1.00000 354.00000 Table 16-16: Attributes of alternatives: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 354 | | Iterations completed 7 | | Log likelihood function -80.49319 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -450.2407 .82122 .82032 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 27.89611 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 1 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .21810419 .11352774 1.921 .0547 TIME -.09229147 .03325675 -2.775 .0055 A_CAR 5.78269901 1.27232203 4.545 .0000 A_CLEVER 3.03705597 1.28674579 2.360 .0183 A_PT .06363630 1.61875999 .039 .9686 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 326.0 observs. | | Coefficient | All 354.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | 219 | ------------------- -------- | -------------------+------------------- | | COST .2181 COST | 5.409 10.390| 5.597 10.776 | | TIME -.0923 TIME | 22.342 20.969| 22.666 21.557 | | A_CAR 5.7827 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 354.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2181 COST | 2.321 4.484| 1.568 1.283 | | TIME -.0923 TIME | 16.839 17.046| 12.182 7.391 | | A_CLEVER 3.0371 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2181 COST | 2.320 3.342| .750 .000 | | TIME -.0923 TIME | 32.560 30.232| 19.500 8.888 | | A_PT .0636 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2181 COST | .282 .213| .050 .000 | | TIME -.0923 TIME | 28.242 21.323| 5.000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 305.00000 20.00000 1.00000 1.00000 326.00000 CLEVER | 20.00000 2.00000 .0000000D+00 .0000000D+00 22.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 330.00000 22.00000 1.00000 1.00000 354.00000 Table 16-17: Attributes of alternatives: Scenario A,B,C (original mode choice) --> NLOGIT;Lhs=CHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 1064 | | Iterations completed 7 | | Log likelihood function -467.4424 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1351.4706 .65412 .65311 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 15.20804 | | Prob [ chi squared > value ] = .00050 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 1 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .07386108 .02975085 2.483 .0130 TIME -.04128280 .01544455 -2.673 .0075 A_CAR 5.46012503 1.19880525 4.555 .0000 A_CLEVER 3.97923266 1.20029464 3.315 .0009 A_PT .22114867 1.56067319 .142 .8873 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 882.0 observs. | | Coefficient | All 1064.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0739 COST | 4.792 9.318| 5.151 10.055 | | TIME -.0413 TIME | 22.358 20.938| 23.376 22.351 | | A_CAR 5.4601 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 176.0 observs. | | Coefficient | All 1064.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | 220 | COST .0739 COST | 2.196 4.272| 1.418 1.772 | | TIME -.0413 TIME | 18.691 18.604| 13.960 8.850 | | A_CLEVER 3.9792 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0739 COST | 2.320 3.342| .750 .000 | | TIME -.0413 TIME | 32.560 30.232| 19.500 8.888 | | A_PT .2211 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0739 COST | .282 .213| .050 .000 | | TIME -.0413 TIME | 28.242 21.323| 5.000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 720.00000 160.00000 1.00000 1.00000 882.00000 CLEVER | 142.00000 34.00000 .0000000D+00 .0000000D+00 176.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 867.00000 195.00000 1.00000 1.00000 1064.00000 Table 16-18: Attributes of alternatives: Scenario A,B,C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 1064 | | Iterations completed 8 | | Log likelihood function -205.4809 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1351.4706 .84796 .84751 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 2] = 62.83849 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 1 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .19664582 .07810953 2.518 .0118 TIME -.09212861 .02372650 -3.883 .0001 A_CAR 5.81488054 1.24504445 4.670 .0000 A_CLEVER 2.97091524 1.25069426 2.375 .0175 A_PT -.09792322 1.65153206 -.059 .9527 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 996.0 observs. | | Coefficient | All 1064.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1966 COST | 4.792 9.318| 4.917 9.592 | | TIME -.0921 TIME | 22.358 20.938| 22.635 21.424 | | A_CAR 5.8149 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 1064.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1966 COST | 2.196 4.272| 1.408 1.287 | | TIME -.0921 TIME | 18.691 18.604| 13.339 7.977 | | A_CLEVER 2.9709 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1966 COST | 2.320 3.342| .750 .000 | 221 | TIME -.0921 TIME | 32.560 30.232| 19.500 8.888 | | A_PT -.0979 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1966 COST | .282 .213| .050 .000 | | TIME -.0921 TIME | 28.242 21.323| 5.000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 941.00000 54.00000 1.00000 1.00000 996.00000 CLEVER | 58.00000 4.00000 .0000000D+00 .0000000D+00 62.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 1004.00000 59.00000 1.00000 1.00000 1064.00000 Table 16-19: Socio-demographic characteristics: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,M MALE,M AGE,M INC1;Wts=P WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 6 | | Log likelihood function -54.35840 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .72873 .72443 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 4] = 15.69600 | | Prob [ chi squared > value ] = .00346 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .03729143 .08954107 .416 .6771 M_MALE .37431769 .62405972 .600 .5486 M_AGE -.03687567 .02117852 -1.741 .0817 M_INC1 .57298395 .38526039 1.487 .1369 A_CAR 3.48931527 1.32034860 2.643 .0082 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0373 COST | 4.587 9.103| 4.691 9.335 | | M_MALE .3743 M_MALE | .533 .500| .535 .500 | | M_AGE -.0369 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .5730 M_INC1 | 2.097 .884| 2.122 .896 | | A_CAR 3.4893 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0373 COST | 2.294 4.552| 1.422 1.447 | | M_MALE .3743 M_MALE | .000 .000| .000 .000 | | M_AGE -.0369 M_AGE | .000 .000| .000 .000 | | M_INC1 .5730 M_INC1 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 288.00000 15.00000 303.00000 CLEVER | 17.00000 1.00000 18.00000 Total | 305.00000 16.00000 321.00000 Table 16-20: Socio-demographic characteristics: Scenario B (corrected mode choice) --> 222 NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME,M MALE,M AGE,M INC1;Wts=P WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 7 | | Log likelihood function -59.52863 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .70292 .69727 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 5] = 28.28082 | | Prob [ chi squared > value ] = .00003 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .34134797 .20291968 1.682 .0925 TIME -.14594291 .05545799 -2.632 .0085 M_MALE .73961045 .56783498 1.303 .1927 M_AGE -.03252881 .01967859 -1.653 .0983 M_INC1 .41430257 .32026200 1.294 .1958 A_CAR 3.44302679 1.17867129 2.921 .0035 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3413 COST | 4.587 9.103| 4.723 9.393 | | TIME -.1459 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .7396 M_MALE | .533 .500| .542 .499 | | M_AGE -.0325 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 .4143 M_INC1 | 2.097 .884| 2.124 .894 | | A_CAR 3.4430 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3413 COST | 2.071 4.122| 1.235 1.189 | | TIME -.1459 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .7396 M_MALE | .000 .000| .000 .000 | | M_AGE -.0325 M_AGE | .000 .000| .000 .000 | | M_INC1 .4143 M_INC1 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 280.00000 19.00000 299.00000 CLEVER | 19.00000 3.00000 22.00000 Total | 299.00000 22.00000 321.00000 Table 16-21: Socio-demographic characteristics: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1;Wts=P_WEIGHT; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 7 | | Log likelihood function -76.01741 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .81014 .80844 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 5] = 31.84956 | | Prob [ chi squared > value ] = .00001 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .16547946 .10994368 1.505 .1323 TIME -.07895641 .03280343 -2.407 .0161 M_MALE .67059894 .51524429 1.302 .1931 M_AGE -.02735022 .01829699 -1.495 .1350 M_INC1 .33165725 .29480569 1.125 .2606 A_CAR 6.21339136 1.68081975 3.697 .0002 A_CLEVER 3.17024404 1.29475603 2.449 .0143 A_PT .16114547 1.59792670 .101 .9197 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | 223 | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1655 COST | 5.530 10.833| 5.752 11.283 | | TIME -.0790 TIME | 22.788 21.615| 23.192 22.303 | | M_MALE .6706 M_MALE | .534 .500| .548 .499 | | M_AGE -.0274 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 .3317 M_INC1 | 2.091 .879| 2.113 .880 | | A_CAR 6.2134 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1655 COST | 2.373 4.675| 1.568 1.283 | | TIME -.0790 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .6706 M_MALE | .000 .000| .000 .000 | | M_AGE -.0274 M_AGE | .000 .000| .000 .000 | | M_INC1 .3317 M_INC1 | .000 .000| .000 .000 | | A_CLEVER 3.1702 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1655 COST | 2.394 3.514| .750 .000 | | TIME -.0790 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .6706 M_MALE | .000 .000| .000 .000 | | M_AGE -.0274 M_AGE | .000 .000| .000 .000 | | M_INC1 .3317 M_INC1 | .000 .000| .000 .000 | | A_PT .1611 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1655 COST | .280 .208| .050 .000 | | TIME -.0790 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .6706 M_MALE | .000 .000| .000 .000 | | M_AGE -.0274 M_AGE | .000 .000| .000 .000 | | M_INC1 .3317 M_INC1 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 271.00000 20.00000 1.00000 1.00000 292.00000 CLEVER | 19.00000 2.00000 .0000000D+00 .0000000D+00 22.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 295.00000 23.00000 1.00000 1.00000 320.00000 Table 16-22: Socio-demographic characteristics: Scenario A, B, C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1;Wts=P_WEIGHT; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 8 | | Log likelihood function -191.8473 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1201.9172 .84038 .83955 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 5] = 77.71216 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .14547056 .07337601 1.983 .0474 TIME -.07876850 .02312923 -3.406 .0007 M_MALE .62446704 .32328135 1.932 .0534 M_AGE -.03224474 .01133201 -2.845 .0044 M_INC1 .42848850 .18940292 2.262 .0237 A_CAR 6.42400728 1.43926897 4.463 .0000 A_CLEVER 3.17430137 1.26839426 2.503 .0123 A_PT .08982750 1.61171288 .056 .9556 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1455 COST | 4.901 9.713| 5.048 10.032 | 224 | TIME -.0788 TIME | 22.805 21.579| 23.147 22.144 | | M_MALE .6245 M_MALE | .533 .499| .541 .499 | | M_AGE -.0322 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 .4285 M_INC1 | 2.095 .881| 2.120 .889 | | A_CAR 6.4240 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1455 COST | 2.246 4.453| 1.408 1.287 | | TIME -.0788 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .6245 M_MALE | .000 .000| .000 .000 | | M_AGE -.0322 M_AGE | .000 .000| .000 .000 | | M_INC1 .4285 M_INC1 | .000 .000| .000 .000 | | A_CLEVER 3.1743 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1455 COST | 2.394 3.514| .750 .000 | | TIME -.0788 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .6245 M_MALE | .000 .000| .000 .000 | | M_AGE -.0322 M_AGE | .000 .000| .000 .000 | | M_INC1 .4285 M_INC1 | .000 .000| .000 .000 | | A_PT .0898 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1455 COST | .280 .208| .050 .000 | | TIME -.0788 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .6245 M_MALE | .000 .000| .000 .000 | | M_AGE -.0322 M_AGE | .000 .000| .000 .000 | | M_INC1 .4285 M_INC1 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 839.00000 54.00000 1.00000 1.00000 894.00000 CLEVER | 56.00000 6.00000 .0000000D+00 .0000000D+00 62.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 900.00000 61.00000 1.00000 1.00000 962.00000 Table 16-23: Mobility pattern: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,M MALE,M AGE,M INC1,M DES,M TRIP;Wts=P WEIGHT; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 7 | | Log likelihood function -48.30863 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .75892 .75354 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 27.79554 | | Prob [ chi squared > value ] = .00010 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST -.11942185 .08585368 -1.391 .1642 M_MALE .55866941 .66318232 .842 .3996 M_AGE -.05465742 .02329838 -2.346 .0190 M_INC1 .47870843 .37863458 1.264 .2061 M_DES .25119485 .40519007 .620 .5353 M_TRIP .02611674 .01098922 2.377 .0175 A_CAR 2.29056176 1.39843300 1.638 .1014 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1194 COST | 4.587 9.103| 4.691 9.335 | | M_MALE .5587 M_MALE | .533 .500| .535 .500 | | M_AGE -.0547 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .4787 M_INC1 | 2.097 .884| 2.122 .896 | | M_DES .2512 M_DES | 2.215 1.496| 2.254 1.524 | | M_TRIP .0261 M_TRIP | 109.156 74.363| 111.680 75.670 | | A_CAR 2.2906 ONE | 1.000 .000| 1.000 .000 | 225 +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1194 COST | 2.294 4.552| 1.422 1.447 | | M_MALE .5587 M_MALE | .000 .000| .000 .000 | | M_AGE -.0547 M_AGE | .000 .000| .000 .000 | | M_INC1 .4787 M_INC1 | .000 .000| .000 .000 | | M_DES .2512 M_DES | .000 .000| .000 .000 | | M_TRIP .0261 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 289.00000 14.00000 303.00000 CLEVER | 16.00000 2.00000 18.00000 Total | 305.00000 16.00000 321.00000 Table 16-24: Mobility pattern: Scenario B (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME,M MALE,M AGE,M INC1,M DES,M TRIP;Wts=P WEIGHT; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 8 | | Log likelihood function -54.24428 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .72930 .72238 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 38.84950 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .27602700 .20859362 1.323 .1857 TIME -.17937655 .06362474 -2.819 .0048 M_MALE .91240674 .60663136 1.504 .1326 M_AGE -.04526709 .02097950 -2.158 .0310 M_INC1 .33929836 .31264424 1.085 .2778 M_DES .37387475 .39421028 .948 .3429 M_TRIP .01929958 .00928834 2.078 .0377 A_CAR 2.28599600 1.24450042 1.837 .0662 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2760 COST | 4.587 9.103| 4.723 9.393 | | TIME -.1794 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .9124 M_MALE | .533 .500| .542 .499 | | M_AGE -.0453 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 .3393 M_INC1 | 2.097 .884| 2.124 .894 | | M_DES .3739 M_DES | 2.215 1.496| 2.271 1.527 | | M_TRIP .0193 M_TRIP | 109.156 74.363| 111.635 76.034 | | A_CAR 2.2860 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2760 COST | 2.071 4.122| 1.235 1.189 | | TIME -.1794 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .9124 M_MALE | .000 .000| .000 .000 | | M_AGE -.0453 M_AGE | .000 .000| .000 .000 | | M_INC1 .3393 M_INC1 | .000 .000| .000 .000 | | M_DES .3739 M_DES | .000 .000| .000 .000 | | M_TRIP .0193 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 281.00000 18.00000 299.00000 CLEVER | 19.00000 3.00000 22.00000 Total | 300.00000 21.00000 321.00000 Table 16-25: Mobility pattern: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M MALE,M AGE,M INC1,M KM, M_BUSI,M_BRIN,M_DES,M_TRIP;Wts=P_WEIGHT;Crosstab;Describe$ 226 Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 8 | | Log likelihood function -67.93084 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .83034 .82843 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 48.02269 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .06275222 .10872699 .577 .5638 TIME -.10962087 .03925463 -2.793 .0052 M_MALE .89507801 .55805683 1.604 .1087 M_AGE -.04091908 .01955054 -2.093 .0364 M_INC1 .20698754 .28551494 .725 .4685 M_DES .56515512 .39206041 1.442 .1494 M_TRIP .02068128 .00861686 2.400 .0164 A_CAR 5.08113631 1.86659048 2.722 .0065 A_CLEVER 3.37582080 1.42657378 2.366 .0180 A_PT .69651608 1.61812840 .430 .6669 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0628 COST | 5.530 10.833| 5.752 11.283 | | TIME -.1096 TIME | 22.788 21.615| 23.192 22.303 | | M_MALE .8951 M_MALE | .534 .500| .548 .499 | | M_AGE -.0409 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 .2070 M_INC1 | 2.091 .879| 2.113 .880 | | M_DES .5652 M_DES | 2.216 1.498| 2.291 1.538 | | M_TRIP .0207 M_TRIP | 109.325 74.418| 113.199 76.215 | | A_CAR 5.0811 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0628 COST | 2.373 4.675| 1.568 1.283 | | TIME -.1096 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .8951 M_MALE | .000 .000| .000 .000 | | M_AGE -.0409 M_AGE | .000 .000| .000 .000 | | M_INC1 .2070 M_INC1 | .000 .000| .000 .000 | | M_DES .5652 M_DES | .000 .000| .000 .000 | | M_TRIP .0207 M_TRIP | .000 .000| .000 .000 | | A_CLEVER 3.3758 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0628 COST | 2.394 3.514| .750 .000 | | TIME -.1096 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .8951 M_MALE | .000 .000| .000 .000 | | M_AGE -.0409 M_AGE | .000 .000| .000 .000 | | M_INC1 .2070 M_INC1 | .000 .000| .000 .000 | | M_DES .5652 M_DES | .000 .000| .000 .000 | | M_TRIP .0207 M_TRIP | .000 .000| .000 .000 | | A_PT .6965 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0628 COST | .280 .208| .050 .000 | | TIME -.1096 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .8951 M_MALE | .000 .000| .000 .000 | | M_AGE -.0409 M_AGE | .000 .000| .000 .000 | | M_INC1 .2070 M_INC1 | .000 .000| .000 .000 | | M_DES .5652 M_DES | .000 .000| .000 .000 | | M_TRIP .0207 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 272.00000 18.00000 1.00000 .0000000D+00 292.00000 CLEVER | 19.00000 3.00000 .0000000D+00 .0000000D+00 22.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 296.00000 22.00000 1.00000 1.00000 320.00000 227 Table 16-26: Mobility pattern: Scenario A, B, C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1,M_KM, M_BUSI,M_BRIN,M_DES,M_TRIP;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 8 | | Log likelihood function -173.3553 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1201.9172 .85577 .85483 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 114.69622 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .03923762 .06817882 .576 .5649 TIME -.10301651 .02692927 -3.825 .0001 M_MALE .81477706 .34465441 2.364 .0181 M_AGE -.04586837 .01208652 -3.795 .0001 M_INC1 .32785971 .18319749 1.790 .0735 M_DES .43716171 .22768293 1.920 .0549 M_TRIP .02009594 .00521434 3.854 .0001 A_CAR 5.42603017 1.54628075 3.509 .0004 A_CLEVER 3.41183397 1.34360595 2.539 .0111 A_PT .68707129 1.59329961 .431 .6663 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0392 COST | 4.901 9.713| 5.048 10.032 | | TIME -.1030 TIME | 22.805 21.579| 23.147 22.144 | | M_MALE .8148 M_MALE | .533 .499| .541 .499 | | M_AGE -.0459 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 .3279 M_INC1 | 2.095 .881| 2.120 .889 | | M_DES .4372 M_DES | 2.215 1.495| 2.272 1.528 | | M_TRIP .0201 M_TRIP | 109.212 74.304| 112.161 75.888 | | A_CAR 5.4260 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0392 COST | 2.246 4.453| 1.408 1.287 | | TIME -.1030 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .8148 M_MALE | .000 .000| .000 .000 | | M_AGE -.0459 M_AGE | .000 .000| .000 .000 | | M_INC1 .3279 M_INC1 | .000 .000| .000 .000 | | M_DES .4372 M_DES | .000 .000| .000 .000 | | M_TRIP .0201 M_TRIP | .000 .000| .000 .000 | | A_CLEVER 3.4118 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0392 COST | 2.394 3.514| .750 .000 | | TIME -.1030 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .8148 M_MALE | .000 .000| .000 .000 | | M_AGE -.0459 M_AGE | .000 .000| .000 .000 | | M_INC1 .3279 M_INC1 | .000 .000| .000 .000 | | M_DES .4372 M_DES | .000 .000| .000 .000 | | M_TRIP .0201 M_TRIP | .000 .000| .000 .000 | | A_PT .6871 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0392 COST | .280 .208| .050 .000 | | TIME -.1030 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .8148 M_MALE | .000 .000| .000 .000 | | M_AGE -.0459 M_AGE | .000 .000| .000 .000 | | M_INC1 .3279 M_INC1 | .000 .000| .000 .000 | | M_DES .4372 M_DES | .000 .000| .000 .000 | | M_TRIP .0201 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +-----------------------------------------------------------------CAR | 843.00000 50.00000 1.00000 .0000000D+00 894.00000 CLEVER | 54.00000 8.00000 .0000000D+00 .0000000D+00 62.00000 228 PT BIKE Total | | | 4.00000 1.00000 901.00000 Table 16-27: .0000000D+00 .0000000D+00 .0000000D+00 4.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 59.00000 1.00000 1.00000 962.00000 Trip related attributes: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,M_MALE,M_AGE,M_INC1,M_BUSI,M_BRIN,M_DES,M_TRIP ;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 31 | | Log likelihood function -47.53024 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .76280 .75596 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 8] = 29.35232 | | Prob [ chi squared > value ] = .00027 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST -.12823859 .09595436 -1.336 .1814 M_MALE .59635334 .66658752 .895 .3710 M_AGE -.05278210 .02330907 -2.264 .0235 M_INC1 .52237766 .38334097 1.363 .1730 M_BUSI 27.8925652 .152113D+07 .000 1.0000 M_BRIN 1.07025625 1.54802499 .691 .4893 M_DES .15239431 .41693075 .366 .7147 M_TRIP .02568138 .01160177 2.214 .0269 A_CAR 2.18431153 1.45521719 1.501 .1334 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1282 COST | 4.587 9.103| 4.691 9.335 | | M_MALE .5964 M_MALE | .533 .500| .535 .500 | | M_AGE -.0528 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .5224 M_INC1 | 2.097 .884| 2.122 .896 | | M_BUSI 27.8926 M_BUSI | .150 .357| .158 .366 | | M_BRIN 1.0703 M_BRIN | .084 .278| .083 .276 | | M_DES .1524 M_DES | 2.215 1.496| 2.254 1.524 | | M_TRIP .0257 M_TRIP | 109.156 74.363| 111.680 75.670 | | A_CAR 2.1843 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1282 COST | 2.294 4.552| 1.422 1.447 | | M_MALE .5964 M_MALE | .000 .000| .000 .000 | | M_AGE -.0528 M_AGE | .000 .000| .000 .000 | | M_INC1 .5224 M_INC1 | .000 .000| .000 .000 | | M_BUSI 27.8926 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.0703 M_BRIN | .000 .000| .000 .000 | | M_DES .1524 M_DES | .000 .000| .000 .000 | | M_TRIP .0257 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 289.00000 14.00000 303.00000 CLEVER | 16.00000 2.00000 18.00000 Total | 306.00000 15.00000 321.00000 Table 16-28: Trip related attributes: Scenario B (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE;Choices=car, CLEVER;Wts=P WEIGHT;Rhs=ONE,COST ,TIME,M_MALE,M_AGE,M_INC1,M_BUSI,M_BRIN,M_DES,M_TRIP;Crosstab;Describe$ +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Jan 23, 2011 at 02:09:16PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 31 | | Log likelihood function -52.49112 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .73804 .72962 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 42.35584 | 229 | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .26110486 .19415548 1.345 .1787 TIME -.20918148 .07023792 -2.978 .0029 M_MALE .80325268 .61732596 1.301 .1932 M_AGE -.04381772 .02096563 -2.090 .0366 M_INC1 .39563147 .31615361 1.251 .2108 M_BUSI 30.3183000 .124483D+07 .000 1.0000 M_BRIN .90681622 1.53647409 .590 .5551 M_DES .24492436 .40304414 .608 .5434 M_TRIP .01745627 .00943101 1.851 .0642 A_CAR 2.59403888 1.31830056 1.968 .0491 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2611 COST | 4.587 9.103| 4.723 9.393 | | TIME -.2092 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .8033 M_MALE | .533 .500| .542 .499 | | M_AGE -.0438 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 .3956 M_INC1 | 2.097 .884| 2.124 .894 | | M_BUSI 30.3183 M_BUSI | .150 .357| .161 .368 | | M_BRIN .9068 M_BRIN | .084 .278| .084 .277 | | M_DES .2449 M_DES | 2.215 1.496| 2.271 1.527 | | M_TRIP .0175 M_TRIP | 109.156 74.363| 111.635 76.034 | | A_CAR 2.5940 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2611 COST | 2.071 4.122| 1.235 1.189 | | TIME -.2092 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .8033 M_MALE | .000 .000| .000 .000 | | M_AGE -.0438 M_AGE | .000 .000| .000 .000 | | M_INC1 .3956 M_INC1 | .000 .000| .000 .000 | | M_BUSI 30.3183 M_BUSI | .000 .000| .000 .000 | | M_BRIN .9068 M_BRIN | .000 .000| .000 .000 | | M_DES .2449 M_DES | .000 .000| .000 .000 | | M_TRIP .0175 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 281.00000 18.00000 299.00000 CLEVER | 19.00000 3.00000 22.00000 Total | 300.00000 21.00000 321.00000 Table 16-29: Trip related attributes: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1, M_BUSI,M_BRIN,M_DES,M_TRIP;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Jan 23, 2011 at 02:23:44PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 31 | | Log likelihood function -66.57966 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .83371 .83147 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 50.72507 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .05652116 .09770301 .578 .5629 TIME -.11549158 .04224425 -2.734 .0063 M_MALE .89385885 .56238472 1.589 .1120 M_AGE -.03884719 .01949055 -1.993 .0462 230 M_INC1 .26116819 .29130237 .897 .3700 M_BUSI 28.3450668 .113518D+07 .000 1.0000 M_BRIN 1.25423286 1.52636944 .822 .4112 M_DES .48168050 .40081869 1.202 .2295 M_TRIP .01861065 .00872994 2.132 .0330 A_CAR 5.25134633 2.01607550 2.605 .0092 A_CLEVER 3.53966977 1.51278865 2.340 .0193 A_PT .86595872 1.65713116 .523 .6013 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0565 COST | 5.530 10.833| 5.752 11.283 | | TIME -.1155 TIME | 22.788 21.615| 23.192 22.303 | | M_MALE .8939 M_MALE | .534 .500| .548 .499 | | M_AGE -.0388 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 .2612 M_INC1 | 2.091 .879| 2.113 .880 | | M_BUSI 28.3451 M_BUSI | .150 .358| .164 .371 | | M_BRIN 1.2542 M_BRIN | .084 .278| .086 .280 | | M_DES .4817 M_DES | 2.216 1.498| 2.291 1.538 | | M_TRIP .0186 M_TRIP | 109.325 74.418| 113.199 76.215 | | A_CAR 5.2513 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0565 COST | 2.373 4.675| 1.568 1.283 | | TIME -.1155 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .8939 M_MALE | .000 .000| .000 .000 | | M_AGE -.0388 M_AGE | .000 .000| .000 .000 | | M_INC1 .2612 M_INC1 | .000 .000| .000 .000 | | M_BUSI 28.3451 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.2542 M_BRIN | .000 .000| .000 .000 | | M_DES .4817 M_DES | .000 .000| .000 .000 | | M_TRIP .0186 M_TRIP | .000 .000| .000 .000 | | A_CLEVER 3.5397 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0565 COST | 2.394 3.514| .750 .000 | | TIME -.1155 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .8939 M_MALE | .000 .000| .000 .000 | | M_AGE -.0388 M_AGE | .000 .000| .000 .000 | | M_INC1 .2612 M_INC1 | .000 .000| .000 .000 | | M_BUSI 28.3451 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.2542 M_BRIN | .000 .000| .000 .000 | | M_DES .4817 M_DES | .000 .000| .000 .000 | | M_TRIP .0186 M_TRIP | .000 .000| .000 .000 | | A_PT .8660 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0565 COST | .280 .208| .050 .000 | | TIME -.1155 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .8939 M_MALE | .000 .000| .000 .000 | | M_AGE -.0388 M_AGE | .000 .000| .000 .000 | | M_INC1 .2612 M_INC1 | .000 .000| .000 .000 | | M_BUSI 28.3451 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.2542 M_BRIN | .000 .000| .000 .000 | | M_DES .4817 M_DES | .000 .000| .000 .000 | | M_TRIP .0186 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 273.00000 18.00000 1.00000 .0000000D+00 292.00000 CLEVER | 19.00000 3.00000 .0000000D+00 .0000000D+00 22.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 297.00000 22.00000 1.00000 1.00000 320.00000 Table 16-30: Trip related attributes: Scenario A, B, C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1, M_BUSI,M_BRIN,M_DES,M_TRIP;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. 231 +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Jan 23, 2011 at 02:33:27PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 32 | | Log likelihood function -169.7942 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1201.9172 .85873 .85762 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 121.81853 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .04396177 .06377739 .689 .4906 TIME -.11315154 .02979653 -3.797 .0001 M_MALE .80553456 .34806821 2.314 .0207 M_AGE -.04368528 .01204725 -3.626 .0003 M_INC1 .38339782 .18660880 2.055 .0399 M_BUSI 29.6327994 .129769D+07 .000 1.0000 M_BRIN 1.14614334 .88386038 1.297 .1947 M_DES .35237813 .23381308 1.507 .1318 M_TRIP .01801589 .00531407 3.390 .0007 A_CAR 5.68666933 1.66450630 3.416 .0006 A_CLEVER 3.67941514 1.42891432 2.575 .0100 A_PT .90238288 1.62263435 .556 .5781 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0440 COST | 4.901 9.713| 5.048 10.032 | | TIME -.1132 TIME | 22.805 21.579| 23.147 22.144 | | M_MALE .8055 M_MALE | .533 .499| .541 .499 | | M_AGE -.0437 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 .3834 M_INC1 | 2.095 .881| 2.120 .889 | | M_BUSI 29.6328 M_BUSI | .150 .357| .161 .368 | | M_BRIN 1.1461 M_BRIN | .084 .278| .084 .277 | | M_DES .3524 M_DES | 2.215 1.495| 2.272 1.528 | | M_TRIP .0180 M_TRIP | 109.212 74.304| 112.161 75.888 | | A_CAR 5.6867 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0440 COST | 2.246 4.453| 1.408 1.287 | | TIME -.1132 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .8055 M_MALE | .000 .000| .000 .000 | | M_AGE -.0437 M_AGE | .000 .000| .000 .000 | | M_INC1 .3834 M_INC1 | .000 .000| .000 .000 | | M_BUSI 29.6328 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.1461 M_BRIN | .000 .000| .000 .000 | | M_DES .3524 M_DES | .000 .000| .000 .000 | | M_TRIP .0180 M_TRIP | .000 .000| .000 .000 | | A_CLEVER 3.6794 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0440 COST | 2.394 3.514| .750 .000 | | TIME -.1132 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .8055 M_MALE | .000 .000| .000 .000 | | M_AGE -.0437 M_AGE | .000 .000| .000 .000 | | M_INC1 .3834 M_INC1 | .000 .000| .000 .000 | | M_BUSI 29.6328 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.1461 M_BRIN | .000 .000| .000 .000 | | M_DES .3524 M_DES | .000 .000| .000 .000 | | M_TRIP .0180 M_TRIP | .000 .000| .000 .000 | | A_PT .9024 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0440 COST | .280 .208| .050 .000 | | TIME -.1132 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .8055 M_MALE | .000 .000| .000 .000 | 232 | M_AGE -.0437 M_AGE | .000 .000| .000 .000 | | M_INC1 .3834 M_INC1 | .000 .000| .000 .000 | | M_BUSI 29.6328 M_BUSI | .000 .000| .000 .000 | | M_BRIN 1.1461 M_BRIN | .000 .000| .000 .000 | | M_DES .3524 M_DES | .000 .000| .000 .000 | | M_TRIP .0180 M_TRIP | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 844.00000 48.00000 1.00000 .0000000D+00 894.00000 CLEVER | 54.00000 8.00000 .0000000D+00 .0000000D+00 62.00000 PT | 4.00000 .0000000D+00 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 903.00000 57.00000 1.00000 .0000000D+00 962.00000 Table 16-31: Subjective motives: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,M_RECOST,M_RETIME,M_CL_1,M_CL_2;Wts=P_WEIGHT ;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 6 | | Log likelihood function -53.35428 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .76319 .75912 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 5] = 20.95007 | | Prob [ chi squared > value ] = .00083 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .04912595 .10168434 .483 .6290 M_RECOST -1.18366426 .58377782 -2.028 .0426 M_RETIME .25308891 1.17026967 .216 .8288 M_CL_1 -.74461948 .44672113 -1.667 .0955 M_CL_2 -.13037954 .35342048 -.369 .7122 A_CAR 5.91721411 1.46212537 4.047 .0001 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 337.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0491 COST | 4.485 8.733| 4.573 8.931 | | M_RECOST -1.1837 M_RECOST | .163 .370| .139 .347 | | M_RETIME .2531 M_RETIME | .039 .195| .039 .193 | | M_CL_1 -.7446 M_CL_1 | 2.699 .995| 2.656 .997 | | M_CL_2 -.1304 M_CL_2 | 2.380 .988| 2.341 .972 | | A_CAR 5.9172 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 355.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0491 COST | 2.242 4.366| 1.422 1.447 | | M_RECOST -1.1837 M_RECOST | .000 .000| .000 .000 | | M_RETIME .2531 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.7446 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.1304 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 324.00000 13.00000 337.00000 CLEVER | 16.00000 2.00000 18.00000 Total | 339.00000 16.00000 355.00000 233 Table 16-32: Subjective motives: Scenario B (corrected mode choice) --> NLOGIT;Lhs=RCHOICE;Choices=car,CLEVER;Rhs=ONE,COST,TIME,M_RECOST,M_RETIME,M_CL_1,M_CL_2;Wts=P_WEIGHT ;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 355 | | Iterations completed 7 | | Log likelihood function -56.76140 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -225.3075 .74807 .74300 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 38.01919 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 0 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .39567493 .17799150 2.223 .0262 TIME -.19065601 .05636536 -3.383 .0007 M_RECOST -.87407320 .56308153 -1.552 .1206 M_RETIME .02375206 1.18213331 .020 .9840 M_CL_1 -.73304689 .45559554 -1.609 .1076 M_CL_2 -.51966458 .35727263 -1.455 .1458 A_CAR 7.23507699 1.63433996 4.427 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 333.0 observs. | | Coefficient | All 355.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3957 COST | 4.485 8.733| 4.600 8.981 | | TIME -.1907 TIME | 22.372 20.947| 22.604 21.390 | | M_RECOST -.8741 M_RECOST | .163 .370| .141 .349 | | M_RETIME .0238 M_RETIME | .039 .195| .039 .194 | | M_CL_1 -.7330 M_CL_1 | 2.699 .995| 2.646 .997 | | M_CL_2 -.5197 M_CL_2 | 2.380 .988| 2.327 .968 | | A_CAR 7.2351 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 355.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3957 COST | 2.024 3.954| 1.235 1.189 | | TIME -.1907 TIME | 16.868 17.030| 11.318 7.344 | | M_RECOST -.8741 M_RECOST | .000 .000| .000 .000 | | M_RETIME .0238 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.7330 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.5197 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 317.00000 16.00000 333.00000 CLEVER | 18.00000 4.00000 22.00000 Total | 334.00000 21.00000 355.00000 Table 16-33: Subjective motives: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_RECOST,M_RETIME, M_CL_1,M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 354 | | Iterations completed 7 | | Log likelihood function -71.43020 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -450.2407 .84135 .83991 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 46.02208 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 1 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .19997727 .12209629 1.638 .1014 TIME -.09074100 .03559147 -2.550 .0108 234 M RECOST M_RETIME M_CL_1 M_CL_2 A_CAR A_CLEVER A_PT -1.29125420 -.06584702 -.57259364 -.24211845 8.72204694 3.04669651 -.01670841 .52894216 .69149797 .40666874 .33632163 1.77911349 1.25818485 1.64931593 -2.441 -.095 -1.408 -.720 4.902 2.422 -.010 .0146 .9241 .1591 .4716 .0000 .0155 .9919 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 326.0 observs. | | Coefficient | All 354.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2000 COST | 5.409 10.390| 5.597 10.776 | | TIME -.0907 TIME | 22.342 20.969| 22.666 21.557 | | M_RECOST -1.2913 M_RECOST | .223 .417| .181 .386 | | M_RETIME -.0658 M_RETIME | .051 .220| .043 .203 | | M_CL_1 -.5726 M_CL_1 | 2.698 .997| 2.638 1.007 | | M_CL_2 -.2421 M_CL_2 | 2.379 .989| 2.319 .975 | | A_CAR 8.7220 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 354.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2000 COST | 2.321 4.484| 1.568 1.283 | | TIME -.0907 TIME | 16.839 17.046| 12.182 7.391 | | M_RECOST -1.2913 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0658 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.5726 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2421 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.0467 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2000 COST | 2.320 3.342| .750 .000 | | TIME -.0907 TIME | 32.560 30.232| 19.500 8.888 | | M_RECOST -1.2913 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0658 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.5726 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2421 M_CL_2 | .000 .000| .000 .000 | | A_PT -.0167 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .2000 COST | .282 .213| .050 .000 | | TIME -.0907 TIME | 28.242 21.323| 5.000 .000 | | M_RECOST -1.2913 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0658 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.5726 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2421 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 308.00000 17.00000 1.00000 1.00000 326.00000 CLEVER | 18.00000 4.00000 .0000000D+00 .0000000D+00 22.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 331.00000 22.00000 1.00000 1.00000 354.00000 Table 16-34: Subjective motives: Scenario A, B, C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_RECOST,M_RETIME, M_CL_1,M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 1064 | | Iterations completed 8 | | Log likelihood function -184.3719 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1351.4706 .86358 .86285 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | 235 | Chi-squared[ 6] = 105.05650 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 1 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .18990330 .07986550 2.378 .0174 TIME -.09583499 .02489772 -3.849 .0001 M_RECOST -1.09870508 .31224536 -3.519 .0004 M_RETIME .02508784 .50874257 .049 .9607 M_CL_1 -.66010520 .24519456 -2.692 .0071 M_CL_2 -.27972861 .19717914 -1.419 .1560 A_CAR 9.09776342 1.47216375 6.180 .0000 A_CLEVER 3.10134614 1.24304972 2.495 .0126 A_PT -.17925505 1.69738080 -.106 .9159 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 996.0 observs. | | Coefficient | All 1064.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1899 COST | 4.792 9.318| 4.917 9.592 | | TIME -.0958 TIME | 22.358 20.938| 22.635 21.424 | | M_RECOST -1.0987 M_RECOST | .183 .387| .154 .361 | | M_RETIME .0251 M_RETIME | .043 .203| .040 .196 | | M_CL_1 -.6601 M_CL_1 | 2.698 .995| 2.647 .999 | | M_CL_2 -.2797 M_CL_2 | 2.380 .988| 2.329 .971 | | A_CAR 9.0978 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 1064.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1899 COST | 2.196 4.272| 1.408 1.287 | | TIME -.0958 TIME | 18.691 18.604| 13.339 7.977 | | M_RECOST -1.0987 M_RECOST | .000 .000| .000 .000 | | M_RETIME .0251 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6601 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2797 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.1013 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 325.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1899 COST | 2.320 3.342| .750 .000 | | TIME -.0958 TIME | 32.560 30.232| 19.500 8.888 | | M_RECOST -1.0987 M_RECOST | .000 .000| .000 .000 | | M_RETIME .0251 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6601 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2797 M_CL_2 | .000 .000| .000 .000 | | A_PT -.1793 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 318.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1899 COST | .282 .213| .050 .000 | | TIME -.0958 TIME | 28.242 21.323| 5.000 .000 | | M_RECOST -1.0987 M_RECOST | .000 .000| .000 .000 | | M_RETIME .0251 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6601 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.2797 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 948.00000 47.00000 1.00000 1.00000 996.00000 CLEVER | 52.00000 10.00000 .0000000D+00 .0000000D+00 62.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 1005.00000 58.00000 1.00000 1.00000 1064.00000 Table 16-35: Subjective motives & sociodemographic characteristics: Scenario A (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,;Choices=car,CLEVER;Rhs=ONE,COST,M_MALE,M_AGE,M_INC1,M_RECOST,M_RETIME,M_CL_1, M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 7 | | Log likelihood function -46.72617 | 236 | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .76681 .76009 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 8] = 30.96047 | | Prob [ chi squared > value ] = .00014 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .03158687 .08439370 .374 .7082 M_MALE -.02391134 .70028946 -.034 .9728 M_AGE -.06935158 .02472685 -2.805 .0050 M_INC1 .38021574 .41647601 .913 .3613 M_RECOST -1.35100411 .66501249 -2.032 .0422 M_RETIME .15234111 1.25543718 .121 .9034 M_CL_1 -1.15271647 .58964839 -1.955 .0506 M_CL_2 -.12080301 .44916458 -.269 .7880 A_CAR 10.0539991 2.70371416 3.719 .0002 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0316 COST | 4.587 9.103| 4.691 9.335 | | M_MALE -.0239 M_MALE | .533 .500| .535 .500 | | M_AGE -.0694 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .3802 M_INC1 | 2.097 .884| 2.122 .896 | | M_RECOST -1.3510 M_RECOST | .181 .385| .155 .363 | | M_RETIME .1523 M_RETIME | .034 .182| .033 .179 | | M_CL_1 -1.1527 M_CL_1 | 2.695 .994| 2.647 .995 | | M_CL_2 -.1208 M_CL_2 | 2.383 1.009| 2.340 .993 | | A_CAR 10.0540 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0316 COST | 2.294 4.552| 1.422 1.447 | | M_MALE -.0239 M_MALE | .000 .000| .000 .000 | | M_AGE -.0694 M_AGE | .000 .000| .000 .000 | | M_INC1 .3802 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.3510 M_RECOST | .000 .000| .000 .000 | | M_RETIME .1523 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -1.1527 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.1208 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 291.00000 12.00000 303.00000 CLEVER | 14.00000 4.00000 18.00000 Total | 305.00000 16.00000 321.00000 Table 16-36: Subjective motives & sociodemographic characteristics: Scenario B (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,;Choices=car,CLEVER;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1,M_RECOST,M_RETIME, M_CL_1,M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 7 | | Log likelihood function -50.69573 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .74700 .73887 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 45.94661 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .37473306 .18973014 1.975 .0483 TIME -.19077609 .06156383 -3.099 .0019 M_MALE .26302205 .64050214 .411 .6813 M_AGE -.06279094 .02236208 -2.808 .0050 M_INC1 .30517455 .35893606 .850 .3952 M_RECOST -.92072976 .62935375 -1.463 .1435 M_RETIME -.07933092 1.23784932 -.064 .9489 M_CL_1 -1.01189537 .57513721 -1.759 .0785 M_CL_2 -.65876305 .43473634 -1.515 .1297 A_CAR 10.9691356 2.74756857 3.992 .0001 +-------------------------------------------------------------------------+ 237 | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3747 COST | 4.587 9.103| 4.723 9.393 | | TIME -.1908 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .2630 M_MALE | .533 .500| .542 .499 | | M_AGE -.0628 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 .3052 M_INC1 | 2.097 .884| 2.124 .894 | | M_RECOST -.9207 M_RECOST | .181 .385| .157 .365 | | M_RETIME -.0793 M_RETIME | .034 .182| .033 .180 | | M_CL_1 -1.0119 M_CL_1 | 2.695 .994| 2.635 .995 | | M_CL_2 -.6588 M_CL_2 | 2.383 1.009| 2.324 .989 | | A_CAR 10.9691 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3747 COST | 2.071 4.122| 1.235 1.189 | | TIME -.1908 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .2630 M_MALE | .000 .000| .000 .000 | | M_AGE -.0628 M_AGE | .000 .000| .000 .000 | | M_INC1 .3052 M_INC1 | .000 .000| .000 .000 | | M_RECOST -.9207 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0793 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -1.0119 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.6588 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 284.00000 15.00000 299.00000 CLEVER | 16.00000 6.00000 22.00000 Total | 300.00000 21.00000 321.00000 Table 16-37: Subjective motives & sociodemographic characteristics: Scenario C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1, M_RECOST,M_RETIME,M_CL_1,M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 7 | | Log likelihood function -64.99127 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .83768 .83549 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 53.90185 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .12530111 .10211617 1.227 .2198 TIME -.06875949 .03150394 -2.183 .0291 M_MALE .54751983 .56595553 .967 .3333 M_AGE -.06892784 .02262462 -3.047 .0023 M_INC1 .05807532 .31236660 .186 .8525 M_RECOST -1.70055529 .60546061 -2.809 .0050 M_RETIME -.12756647 .70915908 -.180 .8572 M_CL_1 -.64888756 .49288314 -1.317 .1880 M_CL_2 -.35149828 .40951141 -.858 .3907 A_CAR 12.6554405 2.57446219 4.916 .0000 A_CLEVER 3.23950627 1.27207545 2.547 .0109 A_PT .05697982 1.64273813 .035 .9723 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1253 COST | 5.530 10.833| 5.752 11.283 | | TIME -.0688 TIME | 22.788 21.615| 23.192 22.303 | | M_MALE .5475 M_MALE | .534 .500| .548 .499 | | M_AGE -.0689 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 .0581 M_INC1 | 2.091 .879| 2.113 .880 | 238 | M RECOST -1.7006 M RECOST | .247 .432| .202 .402 | | M_RETIME -.1276 M_RETIME | .056 .231| .048 .214 | | M_CL_1 -.6489 M_CL_1 | 2.694 .995| 2.627 1.006 | | M_CL_2 -.3515 M_CL_2 | 2.381 1.010| 2.315 .997 | | A_CAR 12.6554 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1253 COST | 2.373 4.675| 1.568 1.283 | | TIME -.0688 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .5475 M_MALE | .000 .000| .000 .000 | | M_AGE -.0689 M_AGE | .000 .000| .000 .000 | | M_INC1 .0581 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.7006 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.1276 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6489 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3515 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.2395 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1253 COST | 2.394 3.514| .750 .000 | | TIME -.0688 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .5475 M_MALE | .000 .000| .000 .000 | | M_AGE -.0689 M_AGE | .000 .000| .000 .000 | | M_INC1 .0581 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.7006 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.1276 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6489 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3515 M_CL_2 | .000 .000| .000 .000 | | A_PT .0570 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1253 COST | .280 .208| .050 .000 | | TIME -.0688 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .5475 M_MALE | .000 .000| .000 .000 | | M_AGE -.0689 M_AGE | .000 .000| .000 .000 | | M_INC1 .0581 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.7006 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.1276 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6489 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3515 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 275.00000 16.00000 1.00000 .0000000D+00 292.00000 CLEVER | 16.00000 6.00000 .0000000D+00 .0000000D+00 22.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 295.00000 23.00000 1.00000 1.00000 320.00000 Table 16-38: Subjective motives & sociodemographic characteristics: Scenario A, B, C (corrected mode choice) --> NLOGIT;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Rhs=ONE,COST,TIME,M_MALE,M_AGE,M_INC1, M_RECOST,M_RETIME,M_CL_1,M_CL_2;Wts=P_WEIGHT;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 8 | | Log likelihood function -165.9425 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1201.9172 .86194 .86085 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 9] = 129.52183 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .13149004 .06856174 1.918 .0551 TIME -.08130700 .02350109 -3.460 .0005 M_MALE .33764069 .35799851 .943 .3456 M_AGE -.06600435 .01325137 -4.981 .0000 M_INC1 .21764468 .20658192 1.054 .2921 239 M RECOST -1.32829274 .35570177 -3.734 .0002 M_RETIME -.02969133 .52113222 -.057 .9546 M_CL_1 -.86572668 .30652223 -2.824 .0047 M_CL_2 -.34701915 .24126507 -1.438 .1503 A_CAR 13.0921269 1.91895420 6.823 .0000 A_CLEVER 3.46805144 1.29329378 2.682 .0073 A_PT -.00959718 1.67923681 -.006 .9954 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1315 COST | 4.901 9.713| 5.048 10.032 | | TIME -.0813 TIME | 22.805 21.579| 23.147 22.144 | | M_MALE .3376 M_MALE | .533 .499| .541 .499 | | M_AGE -.0660 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 .2176 M_INC1 | 2.095 .881| 2.120 .889 | | M_RECOST -1.3283 M_RECOST | .203 .402| .171 .377 | | M_RETIME -.0297 M_RETIME | .042 .200| .038 .191 | | M_CL_1 -.8657 M_CL_1 | 2.694 .993| 2.636 .998 | | M_CL_2 -.3470 M_CL_2 | 2.383 1.008| 2.327 .992 | | A_CAR 13.0921 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1315 COST | 2.246 4.453| 1.408 1.287 | | TIME -.0813 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .3376 M_MALE | .000 .000| .000 .000 | | M_AGE -.0660 M_AGE | .000 .000| .000 .000 | | M_INC1 .2176 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.3283 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0297 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.8657 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3470 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.4681 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1315 COST | 2.394 3.514| .750 .000 | | TIME -.0813 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .3376 M_MALE | .000 .000| .000 .000 | | M_AGE -.0660 M_AGE | .000 .000| .000 .000 | | M_INC1 .2176 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.3283 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0297 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.8657 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3470 M_CL_2 | .000 .000| .000 .000 | | A_PT -.0096 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1315 COST | .280 .208| .050 .000 | | TIME -.0813 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .3376 M_MALE | .000 .000| .000 .000 | | M_AGE -.0660 M_AGE | .000 .000| .000 .000 | | M_INC1 .2176 M_INC1 | .000 .000| .000 .000 | | M_RECOST -1.3283 M_RECOST | .000 .000| .000 .000 | | M_RETIME -.0297 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.8657 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3470 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 850.00000 43.00000 1.00000 .0000000D+00 894.00000 CLEVER | 47.00000 15.00000 .0000000D+00 .0000000D+00 62.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 901.00000 59.00000 1.00000 1.00000 962.00000 Table 16-39: All selected variables: Scenario A (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE;Choices=car, CLEVER;Wts=P_WEIGHT;Rhs=ONE,COST,M_MALE,M_AGE,M_INC1,M_DES,M_TRIP,M_RECOST,M_RETIME,M_CL_1,M_CL_2; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 8 | | Log likelihood function -39.55854 | 240 | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .80258 .79558 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[10] = 45.29571 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST -.12319631 .10771185 -1.144 .2527 M_MALE -.26200796 .87200739 -.300 .7638 M_AGE -.07358800 .02750230 -2.676 .0075 M_INC1 .00918552 .44889389 .020 .9837 M_DES .46230129 .41067063 1.126 .2603 M_TRIP .03509309 .01467348 2.392 .0168 M_RECOST -1.26292663 .74362998 -1.698 .0894 M_RETIME .69891675 1.43456466 .487 .6261 M_CL_1 -1.35652418 .76114775 -1.782 .0747 M_CL_2 -.39936354 .49837415 -.801 .4229 A_CAR 9.26600261 2.81595404 3.291 .0010 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1232 COST | 4.587 9.103| 4.691 9.335 | | M_MALE -.2620 M_MALE | .533 .500| .535 .500 | | M_AGE -.0736 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .0092 M_INC1 | 2.097 .884| 2.122 .896 | | M_DES .4623 M_DES | 2.215 1.496| 2.254 1.524 | | M_TRIP .0351 M_TRIP | 109.156 74.363| 111.680 75.670 | | M_RECOST -1.2629 M_RECOST | .181 .385| .155 .363 | | M_RETIME .6989 M_RETIME | .034 .182| .033 .179 | | M_CL_1 -1.3565 M_CL_1 | 2.695 .994| 2.647 .995 | | M_CL_2 -.3994 M_CL_2 | 2.383 1.009| 2.340 .993 | | A_CAR 9.2660 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.1232 COST | 2.294 4.552| 1.422 1.447 | | M_MALE -.2620 M_MALE | .000 .000| .000 .000 | | M_AGE -.0736 M_AGE | .000 .000| .000 .000 | | M_INC1 .0092 M_INC1 | .000 .000| .000 .000 | | M_DES .4623 M_DES | .000 .000| .000 .000 | | M_TRIP .0351 M_TRIP | .000 .000| .000 .000 | | M_RECOST -1.2629 M_RECOST | .000 .000| .000 .000 | | M_RETIME .6989 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -1.3565 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.3994 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 292.00000 11.00000 303.00000 CLEVER | 12.00000 6.00000 18.00000 Total | 305.00000 16.00000 321.00000 Table 16-40: All selected variables: Scenario B (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE;Choices=car, CLEVER;Wts=P_WEIGHT;Rhs=ONE,COST ,TIME,M_MALE,M_AGE,M_INC1,M_DES,M_TRIP,M_RECOST,M_RETIME,M_CL_1,M_CL_2;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 8 | | Log likelihood function -44.33020 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .77877 .77018 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[11] = 58.67766 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .38169828 .19037497 2.005 .0450 TIME -.24691493 .07895371 -3.127 .0018 M_MALE .16515375 .78661237 .210 .8337 M_AGE -.06945341 .02478751 -2.802 .0051 M_INC1 .08101739 .38607374 .210 .8338 M_DES .51137885 .46138520 1.108 .2677 M_TRIP .02343063 .01173321 1.997 .0458 241 M RECOST M_RETIME M_CL_1 M_CL_2 A_CAR -.98258091 .10235550 -.95969262 -.87924516 9.87333893 .71153157 1.36596119 .66063834 .48521160 2.79188806 -1.381 .075 -1.453 -1.812 3.536 .1673 .9403 .1463 .0700 .0004 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3817 COST | 4.587 9.103| 4.723 9.393 | | TIME -.2469 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .1652 M_MALE | .533 .500| .542 .499 | | M_AGE -.0695 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 .0810 M_INC1 | 2.097 .884| 2.124 .894 | | M_DES .5114 M_DES | 2.215 1.496| 2.271 1.527 | | M_TRIP .0234 M_TRIP | 109.156 74.363| 111.635 76.034 | | M_RECOST -.9826 M_RECOST | .181 .385| .157 .365 | | M_RETIME .1024 M_RETIME | .034 .182| .033 .180 | | M_CL_1 -.9597 M_CL_1 | 2.695 .994| 2.635 .995 | | M_CL_2 -.8792 M_CL_2 | 2.383 1.009| 2.324 .989 | | A_CAR 9.8733 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .3817 COST | 2.071 4.122| 1.235 1.189 | | TIME -.2469 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .1652 M_MALE | .000 .000| .000 .000 | | M_AGE -.0695 M_AGE | .000 .000| .000 .000 | | M_INC1 .0810 M_INC1 | .000 .000| .000 .000 | | M_DES .5114 M_DES | .000 .000| .000 .000 | | M_TRIP .0234 M_TRIP | .000 .000| .000 .000 | | M_RECOST -.9826 M_RECOST | .000 .000| .000 .000 | | M_RETIME .1024 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.9597 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.8792 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 286.00000 13.00000 299.00000 CLEVER | 14.00000 8.00000 22.00000 Total | 300.00000 21.00000 321.00000 Table 16-41: All selected variables: Scenario C (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Wts=P_WEIGHT; Rhs=ONE,COST, ,TIME,M_MALE,M_AGE,M_INC1,M_DES,M_TRIP,M_RECOST,M_RETIME,M_CL_1,M_CL_2;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 8 | | Log likelihood function -54.13423 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .86480 .86266 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[11] = 75.61592 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .04190603 .09384215 .447 .6552 TIME -.11613610 .04360346 -2.663 .0077 M_MALE .39871619 .68085567 .586 .5581 M_AGE -.07374155 .02278991 -3.236 .0012 M_INC1 -.28053772 .31385659 -.894 .3714 M_DES .86717915 .43047046 2.014 .0440 M_TRIP .02418230 .01031203 2.345 .0190 M_RECOST -2.00887005 .63559072 -3.161 .0016 M_RETIME .40233440 .80664175 .499 .6179 M_CL_1 -.66998546 .55438448 -1.209 .2268 M_CL_2 -.71943189 .43910129 -1.638 .1013 A_CAR 12.0971619 2.83253574 4.271 .0000 A_CLEVER 3.37872382 1.47464754 2.291 .0220 A_PT .80066102 1.65113030 .485 .6277 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0419 COST | 5.530 10.833| 5.752 11.283 | | TIME -.1161 TIME | 22.788 21.615| 23.192 22.303 | 242 | M MALE .3987 M MALE | .534 .500| .548 .499 | | M_AGE -.0737 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 -.2805 M_INC1 | 2.091 .879| 2.113 .880 | | M_DES .8672 M_DES | 2.216 1.498| 2.291 1.538 | | M_TRIP .0242 M_TRIP | 109.325 74.418| 113.199 76.215 | | M_RECOST -2.0089 M_RECOST | .247 .432| .202 .402 | | M_RETIME .4023 M_RETIME | .056 .231| .048 .214 | | M_CL_1 -.6700 M_CL_1 | 2.694 .995| 2.627 1.006 | | M_CL_2 -.7194 M_CL_2 | 2.381 1.010| 2.315 .997 | | A_CAR 12.0972 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0419 COST | 2.373 4.675| 1.568 1.283 | | TIME -.1161 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .3987 M_MALE | .000 .000| .000 .000 | | M_AGE -.0737 M_AGE | .000 .000| .000 .000 | | M_INC1 -.2805 M_INC1 | .000 .000| .000 .000 | | M_DES .8672 M_DES | .000 .000| .000 .000 | | M_TRIP .0242 M_TRIP | .000 .000| .000 .000 | | M_RECOST -2.0089 M_RECOST | .000 .000| .000 .000 | | M_RETIME .4023 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6700 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.7194 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.3787 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0419 COST | 2.394 3.514| .750 .000 | | TIME -.1161 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .3987 M_MALE | .000 .000| .000 .000 | | M_AGE -.0737 M_AGE | .000 .000| .000 .000 | | M_INC1 -.2805 M_INC1 | .000 .000| .000 .000 | | M_DES .8672 M_DES | .000 .000| .000 .000 | | M_TRIP .0242 M_TRIP | .000 .000| .000 .000 | | M_RECOST -2.0089 M_RECOST | .000 .000| .000 .000 | | M_RETIME .4023 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6700 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.7194 M_CL_2 | .000 .000| .000 .000 | | A_PT .8007 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0419 COST | .280 .208| .050 .000 | | TIME -.1161 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .3987 M_MALE | .000 .000| .000 .000 | | M_AGE -.0737 M_AGE | .000 .000| .000 .000 | | M_INC1 -.2805 M_INC1 | .000 .000| .000 .000 | | M_DES .8672 M_DES | .000 .000| .000 .000 | | M_TRIP .0242 M_TRIP | .000 .000| .000 .000 | | M_RECOST -2.0089 M_RECOST | .000 .000| .000 .000 | | M_RETIME .4023 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.6700 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.7194 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 277.00000 13.00000 1.00000 .0000000D+00 292.00000 CLEVER | 14.00000 8.00000 .0000000D+00 .0000000D+00 22.00000 PT | 2.00000 2.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 294.00000 25.00000 1.00000 1.00000 320.00000 Table 16-42: All selected variables: Scenario A,B,C (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE,ALT_NO,ALT;Choices=car,CLEVER,pt,bike;Wts=P_WEIGHTH, Rhs=ONE,COST, ,TIME,M_MALE,M_AGE,M_INC1,M_DES,M_TRIP,M_RECOST,M_RETIME,M_CL_1,M_CL_2;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 8 | | Log likelihood function -143.1722 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | 243 | No coefficients -1201.9172 .88088 .87979 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[11] = 175.06249 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .06983850 .07514058 .929 .3527 TIME -.11564394 .03161407 -3.658 .0003 M_MALE .20580095 .42782069 .481 .6305 M_AGE -.06932020 .01390865 -4.984 .0000 M_INC1 -.09027821 .20974968 -.430 .6669 M_DES .67109964 .24859391 2.700 .0069 M_TRIP .02297894 .00635429 3.616 .0003 M_RECOST -1.39417214 .36826509 -3.786 .0002 M_RETIME .31367824 .56193519 .558 .5767 M_CL_1 -.90337462 .34123628 -2.647 .0081 M_CL_2 -.60434690 .25505462 -2.369 .0178 A_CAR 12.2914316 2.07976198 5.910 .0000 A_CLEVER 3.68298767 1.44801254 2.543 .0110 A_PT .91717251 1.63442660 .561 .5747 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0698 COST | 4.901 9.713| 5.048 10.032 | | TIME -.1156 TIME | 22.805 21.579| 23.147 22.144 | | M_MALE .2058 M_MALE | .533 .499| .541 .499 | | M_AGE -.0693 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 -.0903 M_INC1 | 2.095 .881| 2.120 .889 | | M_DES .6711 M_DES | 2.215 1.495| 2.272 1.528 | | M_TRIP .0230 M_TRIP | 109.212 74.304| 112.161 75.888 | | M_RECOST -1.3942 M_RECOST | .203 .402| .171 .377 | | M_RETIME .3137 M_RETIME | .042 .200| .038 .191 | | M_CL_1 -.9034 M_CL_1 | 2.694 .993| 2.636 .998 | | M_CL_2 -.6043 M_CL_2 | 2.383 1.008| 2.327 .992 | | A_CAR 12.2914 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0698 COST | 2.246 4.453| 1.408 1.287 | | TIME -.1156 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .2058 M_MALE | .000 .000| .000 .000 | | M_AGE -.0693 M_AGE | .000 .000| .000 .000 | | M_INC1 -.0903 M_INC1 | .000 .000| .000 .000 | | M_DES .6711 M_DES | .000 .000| .000 .000 | | M_TRIP .0230 M_TRIP | .000 .000| .000 .000 | | M_RECOST -1.3942 M_RECOST | .000 .000| .000 .000 | | M_RETIME .3137 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.9034 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.6043 M_CL_2 | .000 .000| .000 .000 | | A_CLEVER 3.6830 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0698 COST | 2.394 3.514| .750 .000 | | TIME -.1156 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .2058 M_MALE | .000 .000| .000 .000 | | M_AGE -.0693 M_AGE | .000 .000| .000 .000 | | M_INC1 -.0903 M_INC1 | .000 .000| .000 .000 | | M_DES .6711 M_DES | .000 .000| .000 .000 | | M_TRIP .0230 M_TRIP | .000 .000| .000 .000 | | M_RECOST -1.3942 M_RECOST | .000 .000| .000 .000 | | M_RETIME .3137 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.9034 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.6043 M_CL_2 | .000 .000| .000 .000 | | A_PT .9172 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0698 COST | .280 .208| .050 .000 | | TIME -.1156 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .2058 M_MALE | .000 .000| .000 .000 | | M_AGE -.0693 M_AGE | .000 .000| .000 .000 | | M_INC1 -.0903 M_INC1 | .000 .000| .000 .000 | | M_DES .6711 M_DES | .000 .000| .000 .000 | | M_TRIP .0230 M_TRIP | .000 .000| .000 .000 | | M_RECOST -1.3942 M_RECOST | .000 .000| .000 .000 | | M_RETIME .3137 M_RETIME | .000 .000| .000 .000 | | M_CL_1 -.9034 M_CL_1 | .000 .000| .000 .000 | | M_CL_2 -.6043 M_CL_2 | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ 244 Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 856.00000 37.00000 1.00000 .0000000D+00 894.00000 CLEVER | 41.00000 20.00000 .0000000D+00 .0000000D+00 62.00000 PT | 2.00000 2.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 1.00000 1.00000 .0000000D+00 .0000000D+00 2.00000 Total | 900.00000 61.00000 1.00000 1.00000 962.00000 Table 16-43: Influence of awareness with socio-demographic characteristics: Scenario A (merged sample) --> DISCRETECHOICE;Lhs=CHOICE;Choices=car, CLEVER;Wts=P WEIGHT;Rhs=ONE,COST,M MALE,M AGE,M INC1,INFO C; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 02, 2010 at 11:51:29AM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 642 | | Iterations completed 6 | | Log likelihood function -168.9896 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.7638 .57833 .57435 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 5] = 77.22878 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 710, skipped 68 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST -.00861950 .03736086 -.231 .8175 M_MALE -.08486044 .31115923 -.273 .7851 M_AGE .01694323 .01148590 1.475 .1402 M_INC1 1.39947402 .23453170 5.967 .0000 INFO_C 1.53679045 .32333680 4.753 .0000 A_CAR -1.73191110 .68094165 -2.543 .0110 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 573.0 observs. | | Coefficient | All 642.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.0086 COST | 4.587 9.096| 4.767 9.511 | | M_MALE -.0849 M_MALE | .533 .499| .543 .499 | | M_AGE .0169 M_AGE | 49.791 13.146| 50.017 13.024 | | M_INC1 1.3995 M_INC1 | 2.097 .883| 2.176 .886 | | INFO_C 1.5368 INFO_C | .500 .500| .529 .500 | | A_CAR -1.7319 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 69.0 observs. | | Coefficient | All 642.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.0086 COST | 2.294 4.548| 1.549 2.029 | | M_MALE -.0849 M_MALE | .000 .000| .000 .000 | | M_AGE .0169 M_AGE | .000 .000| .000 .000 | | M_INC1 1.3995 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.5368 INFO_C | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 519.00000 54.00000 573.00000 CLEVER | 52.00000 17.00000 69.00000 Total | 570.00000 72.00000 642.00000 Table 16-44: Influence of awareness with socio-demographic characteristics: Scenario B (merged sample) --> DISCRETECHOICE;Lhs=CHOICE;Choices=car, CLEVER;Wts=P WEIGHT;Rhs=ONE,COST,TIEM, M_MALE,M_AGE,M_INC1,INFO_C; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 02, 2010 at 10:41:45AM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 642 | 245 | Iterations completed 6 | | Log likelihood function -185.7604 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.7638 .53648 .53137 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 84.45944 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 710, skipped 68 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .08831989 .05460048 1.618 .1058 TIME -.07969804 .02830548 -2.816 .0049 M_MALE .20842589 .29062488 .717 .4733 M_AGE .01057818 .01076437 .983 .3258 M_INC1 1.06532140 .20040863 5.316 .0000 INFO_C 1.43726535 .30136985 4.769 .0000 A_CAR -.95845942 .64745322 -1.480 .1388 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 563.0 observs. | | Coefficient | All 642.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0883 COST | 4.587 9.096| 4.807 9.590 | | TIME -.0797 TIME | 22.819 21.572| 23.437 22.544 | | M_MALE .2084 M_MALE | .533 .499| .549 .498 | | M_AGE .0106 M_AGE | 49.791 13.146| 50.021 13.110 | | M_INC1 1.0653 M_INC1 | 2.097 .883| 2.176 .884 | | INFO_C 1.4373 INFO_C | .500 .500| .531 .499 | | A_CAR -.9585 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 79.0 observs. | | Coefficient | All 642.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0883 COST | 2.071 4.119| 1.359 1.713 | | TIME -.0797 TIME | 17.212 17.483| 12.342 7.836 | | M_MALE .2084 M_MALE | .000 .000| .000 .000 | | M_AGE .0106 M_AGE | .000 .000| .000 .000 | | M_INC1 1.0653 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4373 INFO_C | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 500.00000 63.00000 563.00000 CLEVER | 59.00000 20.00000 79.00000 Total | 559.00000 83.00000 642.00000 Table 16-45: Influence of awareness with socio-demographic characteristics: Scenario C (merged sample) --> DISCRETECHOICE;Lhs=CHOICE,ALT NO,ALT;Choices=car, CLEVER,pt,bike;Wts= P WEIGHT;Rhs=ONE,COST,TIEM, M_MALE,M_AGE,M_INC1,INFO_C; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 02, 2010 at 00:13:19PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 640 | | Iterations completed 7 | | Log likelihood function -225.8269 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -800.7791 .71799 .71657 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 88.09538 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 710, skipped 70 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .19766678 .06676283 2.961 .0031 TIME -.06990023 .02219259 -3.150 .0016 M_MALE -.08679095 .26810891 -.324 .7462 M_AGE .01851092 .01014237 1.825 .0680 M_INC1 .81223453 .17952324 4.524 .0000 INFO_C 1.43228271 .27512619 5.206 .0000 A_CAR 2.46300278 1.06503631 2.313 .0207 A_CLEVER 3.71479136 .86687334 4.285 .0000 A_PT .24354735 1.10074392 .221 .8249 246 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 542.0 observs. | | Coefficient | All 640.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1977 COST | 5.530 10.824| 5.984 11.642 | | TIME -.0699 TIME | 22.788 21.598| 23.779 22.910 | | M_MALE -.0868 M_MALE | .534 .499| .554 .498 | | M_AGE .0185 M_AGE | 49.784 13.166| 50.011 13.259 | | M_INC1 .8122 M_INC1 | 2.091 .878| 2.149 .854 | | INFO_C 1.4323 INFO_C | .500 .500| .539 .499 | | A_CAR 2.4630 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 86.0 observs. | | Coefficient | All 640.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1977 COST | 2.373 4.671| 1.378 1.280 | | TIME -.0699 TIME | 17.181 17.501| 12.174 7.187 | | M_MALE -.0868 M_MALE | .000 .000| .000 .000 | | M_AGE .0185 M_AGE | .000 .000| .000 .000 | | M_INC1 .8122 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4323 INFO_C | .000 .000| .000 .000 | | A_CLEVER 3.7148 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 8.0 observs. | | Coefficient | All 584.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1977 COST | 2.394 3.511| .750 .000 | | TIME -.0699 TIME | 32.767 31.152| 19.500 8.229 | | M_MALE -.0868 M_MALE | .000 .000| .000 .000 | | M_AGE .0185 M_AGE | .000 .000| .000 .000 | | M_INC1 .8122 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4323 INFO_C | .000 .000| .000 .000 | | A_PT .2435 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 4.0 observs. | | Coefficient | All 576.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1977 COST | .280 .208| .050 .000 | | TIME -.0699 TIME | 27.983 20.793| 5.000 .000 | | M_MALE -.0868 M_MALE | .000 .000| .000 .000 | | M_AGE .0185 M_AGE | .000 .000| .000 .000 | | M_INC1 .8122 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4323 INFO_C | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 470.00000 69.00000 1.00000 1.00000 542.00000 CLEVER | 64.00000 22.00000 1.00000 .0000000D+00 86.00000 PT | 7.00000 1.00000 .0000000D+00 .0000000D+00 8.00000 BIKE | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 Total | 544.00000 93.00000 2.00000 1.00000 640.00000 Table 16-46: Influence of awareness with socio-demographic characteristics: Scenario A,B,C (merged sample) --> DISCRETECHOICE;Lhs=CHOICE,ALT NO,ALT;Choices=car, CLEVER,pt,bike;Wts= P WEIGHT;Rhs=ONE,COST,TIEM, M_MALE,M_AGE,M_INC1,INFO_C; Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 08, 2010 at 01:05:40PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 1924 | | Iterations completed 7 | | Log likelihood function -588.4617 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -2403.8344 .75520 .75448 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 246.13041 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 2130, skipped 206 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ 247 COST .07599891 .02988228 2.543 .0110 TIME -.05254957 .01326797 -3.961 .0001 M_MALE .02958763 .16530028 .179 .8579 M_AGE .01464967 .00618029 2.370 .0178 M_INC1 1.05558938 .11557680 9.133 .0000 INFO_C 1.44984842 .17104125 8.477 .0000 A_CAR 2.30023054 .92909624 2.476 .0133 A_CLEVER 3.57658170 .85232700 4.196 .0000 A_PT .23210696 1.10518682 .210 .8337 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 1678.0 observs. | | Coefficient | All 1924.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0760 COST | 4.901 9.710| 5.174 10.282 | | TIME -.0525 TIME | 22.805 21.573| 23.549 22.633 | | M_MALE .0296 M_MALE | .533 .499| .548 .498 | | M_AGE .0146 M_AGE | 49.789 13.145| 50.017 13.121 | | M_INC1 1.0556 M_INC1 | 2.095 .881| 2.167 .875 | | INFO_C 1.4498 INFO_C | .500 .500| .533 .499 | | A_CAR 2.3002 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 234.0 observs. | | Coefficient | All 1924.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0760 COST | 2.246 4.452| 1.422 1.670 | | TIME -.0525 TIME | 19.069 19.130| 13.812 8.687 | | M_MALE .0296 M_MALE | .000 .000| .000 .000 | | M_AGE .0146 M_AGE | .000 .000| .000 .000 | | M_INC1 1.0556 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4498 INFO_C | .000 .000| .000 .000 | | A_CLEVER 3.5766 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 8.0 observs. | | Coefficient | All 584.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0760 COST | 2.394 3.511| .750 .000 | | TIME -.0525 TIME | 32.767 31.152| 19.500 8.229 | | M_MALE .0296 M_MALE | .000 .000| .000 .000 | | M_AGE .0146 M_AGE | .000 .000| .000 .000 | | M_INC1 1.0556 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4498 INFO_C | .000 .000| .000 .000 | | A_PT .2321 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 4.0 observs. | | Coefficient | All 576.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0760 COST | .280 .208| .050 .000 | | TIME -.0525 TIME | 27.983 20.793| 5.000 .000 | | M_MALE .0296 M_MALE | .000 .000| .000 .000 | | M_AGE .0146 M_AGE | .000 .000| .000 .000 | | M_INC1 1.0556 M_INC1 | .000 .000| .000 .000 | | INFO_C 1.4498 INFO_C | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 1489.00000 186.00000 2.00000 1.00000 1678.00000 CLEVER | 175.00000 58.00000 .0000000D+00 .0000000D+00 234.00000 PT | 7.00000 1.00000 .0000000D+00 .0000000D+00 8.00000 BIKE | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 Total | 1674.00000 246.00000 2.00000 1.00000 1924.00000 Table 16-47: Methodological influence with socio-demographic characteristics: Scenario A (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE;Choices=car, CLEVER;Wts=P WEIGHT;Rhs=ONE,COST ,M_MALE,M_AGE,M_INC1,M_INT_NO,M_INTMIN;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 08, 2010 at 03:33:29PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 7 | | Log likelihood function -46.13888 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .76975 .76461 | 248 | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 6] = 32.13503 | | Prob [ chi squared > value ] = .00002 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST -.01458314 .07422927 -.196 .8442 M_MALE .20486746 .68787595 .298 .7658 M_AGE -.04525735 .02208819 -2.049 .0405 M_INC1 .60278084 .38051419 1.584 .1132 M_INT_NO .19996716 .07415378 2.697 .0070 M_INTMIN -.01444425 .01258184 -1.148 .2510 A_CAR 3.76128856 1.44847578 2.597 .0094 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 303.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.0146 COST | 4.587 9.103| 4.691 9.335 | | M_MALE .2049 M_MALE | .533 .500| .535 .500 | | M_AGE -.0453 M_AGE | 49.791 13.156| 49.578 12.962 | | M_INC1 .6028 M_INC1 | 2.097 .884| 2.122 .896 | | M_INT_NO .2000 M_INT_NO | 12.526 10.347| 13.000 10.425 | | M_INTMIN -.0144 M_INTMIN | 72.897 22.403| 73.168 22.787 | | A_CAR 3.7613 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 18.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST -.0146 COST | 2.294 4.552| 1.422 1.447 | | M_MALE .2049 M_MALE | .000 .000| .000 .000 | | M_AGE -.0453 M_AGE | .000 .000| .000 .000 | | M_INC1 .6028 M_INC1 | .000 .000| .000 .000 | | M_INT_NO .2000 M_INT_NO | .000 .000| .000 .000 | | M_INTMIN -.0144 M_INTMIN | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 291.00000 12.00000 303.00000 CLEVER | 16.00000 2.00000 18.00000 Total | 307.00000 14.00000 321.00000 Table 16-48: Methodological influence with socio-demographic characteristics: Scenario B (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE;Choices=car, CLEVER;Wts=P WEIGHT;Rhs=ONE,COST ,TIME,M_MALE,M_AGE,M_INC1,M_INT1,M_INTMIN;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 05, 2010 at 03:28:28PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 321 | | Iterations completed 8 | | Log likelihood function -42.77056 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -200.3819 .78655 .78110 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 61.79696 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 34 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .12563450 .14720564 .853 .3934 TIME -.12955059 .06050454 -2.141 .0323 M_MALE .76718952 .67800220 1.132 .2578 M_AGE -.03059847 .02329982 -1.313 .1891 M_INC1 -.93843374 .46154813 -2.033 .0420 M_INT1 -2.77746594 .63447177 -4.378 .0000 M_INTMIN -.03879012 .01444107 -2.686 .0072 A_CAR 14.7940423 2.89625913 5.108 .0000 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 299.0 observs. | | Coefficient | All 321.0 obs.|that chose CAR | 249 | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1256 COST | 4.587 9.103| 4.723 9.393 | | TIME -.1296 TIME | 22.819 21.589| 23.110 22.107 | | M_MALE .7672 M_MALE | .533 .500| .542 .499 | | M_AGE -.0306 M_AGE | 49.791 13.156| 49.619 13.044 | | M_INC1 -.9384 M_INC1 | 2.097 .884| 2.124 .894 | | M_INT1 -2.7775 M_INT1 | 1.564 .764| 1.498 .730 | | M_INTMIN -.0388 M_INTMIN | 72.897 22.403| 72.943 22.855 | | A_CAR 14.7940 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 321.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .1256 COST | 2.071 4.122| 1.235 1.189 | | TIME -.1296 TIME | 17.212 17.496| 11.318 7.344 | | M_MALE .7672 M_MALE | .000 .000| .000 .000 | | M_AGE -.0306 M_AGE | .000 .000| .000 .000 | | M_INC1 -.9384 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.7775 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0388 M_INTMIN | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 3 rows and 3 columns. CAR CLEVER Total +-----------------------------------------CAR | 285.00000 14.00000 299.00000 CLEVER | 16.00000 6.00000 22.00000 Total | 301.00000 20.00000 321.00000 Table 16-49: Methodological influence with socio-demographic characteristics: Scenario C (corrected mode choice) --> DISCRETECHOICE;Lhs=RCHOICE,ALT NO,ALT;Choices=car,CLEVER,pt,bike;Wts=P WEIGHT, ONE,COST,TIME, M_MALE,M_AGE,M_INC1,M_INT1,M_INTMIN;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 01, 2010 at 01:22:03PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 320 | | Iterations completed 7 | | Log likelihood function -62.22406 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -400.3895 .84459 .84285 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 59.43626 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 355, skipped 35 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .06336555 .09068540 .699 .4847 TIME -.06245616 .03050536 -2.047 .0406 M_MALE .52241845 .57852053 .903 .3665 M_AGE -.02433319 .02026922 -1.200 .2299 M_INC1 -.58359830 .39728364 -1.469 .1418 M_INT1 -2.05355158 .47043902 -4.365 .0000 M_INTMIN -.03818139 .01237544 -3.085 .0020 A_CAR 14.5881959 2.50550285 5.822 .0000 A_CLEVER 2.94646012 1.23295612 2.390 .0169 A_PT .13029513 1.59806919 .082 .9350 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 292.0 observs. | | Coefficient | All 320.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0634 COST | 5.530 10.833| 5.752 11.283 | | TIME -.0625 TIME | 22.788 21.615| 23.192 22.303 | | M_MALE .5224 M_MALE | .534 .500| .548 .499 | | M_AGE -.0243 M_AGE | 49.784 13.176| 49.507 13.100 | | M_INC1 -.5836 M_INC1 | 2.091 .879| 2.113 .880 | | M_INT1 -2.0536 M_INT1 | 1.563 .765| 1.493 .734 | | M_INTMIN -.0382 M_INTMIN | 72.938 22.426| 73.151 22.930 | | A_CAR 14.5882 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 22.0 observs. | | Coefficient | All 320.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0634 COST | 2.373 4.675| 1.568 1.283 | 250 | TIME -.0625 TIME | 17.181 17.515| 12.182 7.391 | | M_MALE .5224 M_MALE | .000 .000| .000 .000 | | M_AGE -.0243 M_AGE | .000 .000| .000 .000 | | M_INC1 -.5836 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.0536 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0382 M_INTMIN | .000 .000| .000 .000 | | A_CLEVER 2.9465 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0634 COST | 2.394 3.514| .750 .000 | | TIME -.0625 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .5224 M_MALE | .000 .000| .000 .000 | | M_AGE -.0243 M_AGE | .000 .000| .000 .000 | | M_INC1 -.5836 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.0536 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0382 M_INTMIN | .000 .000| .000 .000 | | A_PT .1303 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0634 COST | .280 .208| .050 .000 | | TIME -.0625 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .5224 M_MALE | .000 .000| .000 .000 | | M_AGE -.0243 M_AGE | .000 .000| .000 .000 | | M_INC1 -.5836 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.0536 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0382 M_INTMIN | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 274.00000 17.00000 1.00000 1.00000 292.00000 CLEVER | 17.00000 5.00000 .0000000D+00 .0000000D+00 22.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 296.00000 22.00000 1.00000 1.00000 320.00000 Table 16-50: Methodological influence with socio-demographic characteristics: Scenario A,B,C (corrected mode choice) DISCRETECHOICE;Lhs=RCHOICE,ALT NO,ALT;Choices=car,CLEVER,pt,bike;Wts=P WEIGHT, ONE,COST,TIME,M_MALE,M_AGE,M_INC1,M_INT1,M_INTMIN;Crosstab;Describe$ Normal exit from iterations. Exit status=0. +---------------------------------------------+ | Discrete choice (multinomial logit) model | | Maximum Likelihood Estimates | | Model estimated: Mar 01, 2010 at 01:41:57PM.| | Dependent variable Choice | | Weighting variable P_WEIGHT | | Number of observations 962 | | Iterations completed 8 | | Log likelihood function -145.4471 | | R2=1-LogL/LogL* Log-L fncn R-sqrd RsqAdj | | No coefficients -1201.9172 .87899 .87820 | | Constants only. Must be computed directly. | | Use NLOGIT ;...; RHS=ONE $ | | Chi-squared[ 7] = 170.51258 | | Prob [ chi squared > value ] = .00000 | | Response data are given as ind. choice. | | Number of obs.= 1065, skipped 103 bad obs. | +---------------------------------------------+ +---------+--------------+----------------+--------+---------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | +---------+--------------+----------------+--------+---------+ COST .03785798 .05238451 .723 .4699 TIME -.06478811 .02183748 -2.967 .0030 M_MALE .51407438 .37094142 1.386 .1658 M_AGE -.02751109 .01282846 -2.145 .0320 M_INC1 -.76578093 .26064955 -2.938 .0033 M_INT1 -2.47420475 .32853166 -7.531 .0000 M_INTMIN -.03882517 .00781781 -4.966 .0000 A_CAR 16.2560988 1.91595869 8.485 .0000 A_CLEVER 2.91569773 1.22308141 2.384 .0171 A_PT .02557739 1.62500401 .016 .9874 +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CAR : | Utility Function | | 894.0 observs. | | Coefficient | All 962.0 obs.|that chose CAR | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0379 COST | 4.901 9.713| 5.048 10.032 | | TIME -.0648 TIME | 22.805 21.579| 23.147 22.144 | 251 | M MALE .5141 M MALE | .533 .499| .541 .499 | | M_AGE -.0275 M_AGE | 49.789 13.149| 49.568 13.020 | | M_INC1 -.7658 M_INC1 | 2.095 .881| 2.120 .889 | | M_INT1 -2.4742 M_INT1 | 1.563 .764| 1.499 .733 | | M_INTMIN -.0388 M_INTMIN | 72.911 22.387| 73.087 22.831 | | A_CAR 16.2561 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative CLEVER : | Utility Function | | 62.0 observs. | | Coefficient | All 962.0 obs.|that chose CLEVER | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0379 COST | 2.246 4.453| 1.408 1.287 | | TIME -.0648 TIME | 19.069 19.135| 13.339 7.977 | | M_MALE .5141 M_MALE | .000 .000| .000 .000 | | M_AGE -.0275 M_AGE | .000 .000| .000 .000 | | M_INC1 -.7658 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.4742 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0388 M_INTMIN | .000 .000| .000 .000 | | A_CLEVER 2.9157 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative PT : | Utility Function | | 4.0 observs. | | Coefficient | All 292.0 obs.|that chose PT | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0379 COST | 2.394 3.514| .750 .000 | | TIME -.0648 TIME | 32.767 31.179| 19.500 8.888 | | M_MALE .5141 M_MALE | .000 .000| .000 .000 | | M_AGE -.0275 M_AGE | .000 .000| .000 .000 | | M_INC1 -.7658 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.4742 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0388 M_INTMIN | .000 .000| .000 .000 | | A_PT .0256 ONE | 1.000 .000| 1.000 .000 | +-------------------------------------------------------------------------+ +-------------------------------------------------------------------------+ | Descriptive Statistics for Alternative BIKE : | Utility Function | | 2.0 observs. | | Coefficient | All 288.0 obs.|that chose BIKE | | Name Value Variable | Mean Std. Dev.|Mean Std. Dev. | | ------------------- -------- | -------------------+------------------- | | COST .0379 COST | .280 .208| .050 .000 | | TIME -.0648 TIME | 27.983 20.811| 5.000 .000 | | M_MALE .5141 M_MALE | .000 .000| .000 .000 | | M_AGE -.0275 M_AGE | .000 .000| .000 .000 | | M_INC1 -.7658 M_INC1 | .000 .000| .000 .000 | | M_INT1 -2.4742 M_INT1 | .000 .000| .000 .000 | | M_INTMIN -.0388 M_INTMIN | .000 .000| .000 .000 | +-------------------------------------------------------------------------+ +------------------------------------------------------+ | Cross tabulation of actual vs. predicted choices. | | Row indicator is actual, column is predicted. | | Predicted total is F(k,j,i)=Sum(i=1,...,N) P(k,j,i). | | Column totals may be subject to rounding error. | +------------------------------------------------------+ Matrix Crosstab has 5 rows and 5 columns. CAR CLEVER PT BIKE Total +---------------------------------------------------------------------CAR | 850.00000 43.00000 1.00000 1.00000 894.00000 CLEVER | 47.00000 14.00000 .0000000D+00 .0000000D+00 62.00000 PT | 3.00000 1.00000 .0000000D+00 .0000000D+00 4.00000 BIKE | 2.00000 .0000000D+00 .0000000D+00 .0000000D+00 2.00000 Total | 903.00000 58.00000 1.00000 1.00000 962.00000 252 17 Curriculum vitae Personal Data Name Dipl.-Ing. Sandra Wegener (née Hanzl) Day of birth 31st August 1974 Place of birth Vienna State Austria Citizenship Austria Family status Married with Dipl.-Ing. Stefan Wegener, 2 daugthers (Emma & Marlies) University University of Natural Resources and Life Sciences, Vienna Education 1993 General qualification for university entrance at BG X Laaerbergstrasse, Vienna University of Natural Resources and Life Sciences, Vienna: Landscape Planning Job History 2001 Final Degree; Diploma Thesis at the Institute for Transport Studies “Cycling in pedestrian zones” 2011 Doctorate Degree; Thesis at the Institute for Transport Studies 1999 – 2000 Project work at the Austrian Road Safety Board since 2001 Vienna Researcher at the Institute for Transport Studies, University of Natural Resources and Life Sciences, Vienna March 2011 253