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:
ƒ
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,
ƒ
to analyse influencing factors as well as barriers for or against the choice of the
innovative vehicle,
ƒ
to check critically the survey method particularly in terms of the plausibility of the
hypothetical mode choice,
ƒ
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:
ƒ
What kind of mode choice model is covering the use of the defined, new vehicle best and
which influencing variables are significant?
ƒ
Who are the target persons for its use, how old are they, are they male or female etc.?
ƒ
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.?
ƒ
What kinds of motives or barriers emerge deciding to use or not to use the new vehicle?
ƒ
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?
ƒ
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)]:
ƒ
Towards free-flowing towns and cities, aiming at combating congestion,
ƒ
Towards greener towns and cities, tackling the main environmental issues (CO2, air
pollutant emissions and noise),
ƒ
Towards smarter urban transport, promoting the development and use of Intelligent
Transport Systems,
ƒ
Towards accessible urban transport, alleviating accessibility to mobility for all people
living in cities,
ƒ
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):
ƒ
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.
ƒ
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
ƒ
Area wide supply of gas stations for vehicles like CLEVER run by compressed natural
gas (CNG)
ƒ
Sales and distribution of CLEVER in the whole country
ƒ
Good service supply (garages etc.) for
CLEVER
ƒ
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
ƒ
No parking fees and no time limitations in on-street parking
zones for CLEVER;
ƒ
Reduced parking fees (– 50%) in garages for CLEVER
ƒ
Priority parking sites for CLEVER in the city centre and at P&R locations
(in garages and on-street)
ƒ
Use of bus lanes with CLEVER in the city
ƒ
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:
ƒ
the decision maker: who is the decision maker and what are his/her characteristics;
ƒ
the alternatives: determine possible options of the decision maker;
ƒ
the attributes: identify the attributes of each potential alternative that the decision
maker is taking into account to make his/her decision;
ƒ
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
ƒ Modal Shift in the 3 Scenarios
ƒ Traffic volume [vehicle-km]
ƒ Air Pollutants & CO2-emissions
ƒ Noise
Costs
Values
ƒ Fuel consumption &
Running costs
ƒ Road accidents
ƒ Journey time
ƒ Parking Infrastructure
ƒ 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.
Summing up, the potential of the new eco city car as well as the positive effects resulting
from its use have been verified, however, total benefits for the environment and for urban
traffic can just be gained if only car drivers change to CLEVER and not those people who
used eco friendly modes anyway.
196
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(2008):
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Großzählung
2001.
Ausgewählte
Maßzahlen
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Additional Internet Links:
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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