Content • Quick Review of Major Concepts from Last Week Accessibility (continued) & Basics of Travel Demand – Accessibility measures via Gravity Model and Utility-based Model • Conclusions from Accessibility Lecture – “Composite Measures,” Deciding on a “Best Measure”, Accessibility as raison d’etre? • Travel Demand – – – – – Day 6 11.953 Basic Characteristics Primary Drivers Influencing Factors International Comparisons Implications for the Future… • Assignment I • Other Course Logistic Items Back to the “Four Step” Gravity-based Measures • Theoretical origins in physics, • Improvement over distance-based measures, partly because they attempt to better reflect travel behavior realities through their functional form, generally: Ai = ∑ W j f (ci j , β ) j Trip Generation Predicts number of trips produced and attracted in a given zone Trip Distribution Produces trip production and attraction for each zone Modal Split • where: – – – – Data Inputs Inventories and forecasts of population, land uses, travel behavior, etc. Predicts mode share typically for auto and public transport (can include walk, bike) Wj represents the opportunities available in a given zone j; f(cij, β) = exp (- βcij) = impedance between zones i and j; cij represents the travel cost/distance between zones i and j; and β is a travel cost sensitivity parameter. • generally enters as a negative exponential function • the accessibility measure clearly is highly sensitive to this parameter. • Should come from empirical analysis Utility-Based Accessibility: the Logit Model Trip Assignment Assigns trips to their respective networks System Outputs Provides, for each link, data including traffic volumes, speeds, vehicle mix Evaluation Back to the “Four Step” Data Inputs Inventories and forecasts of population, land uses, travel behavior, etc. Pn (i) = e μVin j ∑e j =1 μV jn Example: Car or Bus? • Potential Influencing factors (variables) – In-vehicle travel time – Out-of-vehicle travel time – Traveler income – Age – Gender – Etc. Normally, Results used to MAKE PREDICTIONS about choices in some future (or alternative)setting Trip Generation Predicts number of trips produced and attracted in a given zone Trip Distribution Produces trip production and attraction for each zone Modal Split Predicts mode share typically for auto and public transport (can include walk, bike) Trip Assignment Assigns trips to their respective networks System Outputs Provides, for each link, data including traffic volumes, speeds, vehicle mix Evaluation 1 Utility-Based Accessibility: The “Logsum” and Nested Logit Pn(dm) = Pn(m|d)Pn(d) “Logsum” at “the root” represents composite benefit (“Expected Maximum Utility”) of the entire choice process e(Vd + V ' d ) μ Pn (d ) = L 2. Destination Choice Disturbance term = εd Scale parameter = μd d ∑ e(Vd +V 'd ) μ Traffic Analysis Zone (TAZ) d d ∈D n V 'd = 1 μm ln ∑ e (Vm +Vdm ) μ m d1 d2 d3 L 1. Mode Choice Disturbance term = εdm Scale parameter = μm m∈M nd e(Vm + Vdm ) μ m Pn (m | d ) = m1 ∑ e(Vm +Vdm ) μ m m2 m3 m∈ M nd Social Accessibility Levels Female Adult, Evaluated at Mean Relevant Characteristics for Income Category High Income Middle Income Low Income “Utility-based” Measures • Theoretically appealing – Basis in behavioral theory and welfare economics • Not immediately and easily convertible into meaningful and understandable units – Convertible into currency, time, but cumbersome • Assumes utility linear with respect to income – Nonpresence of income effect • Still travel-biased measures – Cannot immediately account for non trip-based accessibility (e.g., not traveling; trip-chaining) Relative Decline in Recreational Accessibility Middle Income Female Loss of Auto Loss of Bike Loss of Metro “Composite” or “Activity-based” Approaches • Essentially merging person-based (timespace) with utility-based • Aims to account for people’s activities throughout the day. • Directly linked to “activity-based” travel research – Reflect activity re-scheduling, work-at-home possibilities, etc. • Data and computationally intensive 2 Activity-Based Example: Long-Term Impacts of Congestion Pricing “Best” Measure? • No universally-agreed upon criteria • An “ideal” accessibility measure should reflect: – Different preferences among people, – Scarcity of people’s time and money, – Range of relevant travel (“impedance”) characteristics • safety, convenience, comfort, aesthetics, etc.; – Range of destination (“opportunity”) characteristics: • safety, convenience, aesthetics, diversity, etc. – Relevant traveler characteristics • vehicle availability, age, disability status, etc. – And be “operational,” interpretable, easily communicated. Activity (ABA) versus Trip (TBA) - Total effects lower (mean and median) for ABA - ABA accounts for shifting to non-peak, change in activity pattern (e.g., work at home) ¾ The composite, activity-based approach approaches the theoretical ideal. See, e.g.: Ramming, 1994; Bhat et al, 2000; Handy and Clifton, 2001; Geurs and van Wee, 2004 Dong et al, 2005. Accessibility: Indicator or Variable? Accessibility as LUT raison d’etre? • Examples here have shown accessibility as Indicator – US Cities accessibility – Neighborhood variation (Limanond and Niemeier, 2003) – Total User Benefits (Martinez and Araya, 2000) • Accessibility also used as variable (input) – As determinant of some behavior or activity, influencing, e.g., residential choice, mode choice, vehicle ownership, etc. – Household’s worker(s) commute time(s) influencing residential choice – distance to bus stops; – “neighborhood accessibility”; – “transit accessibility”; – number of jobs within certain driving distance; – distance to CBD; – employment density within certain radii; – number of establishments within various radii of home – even population density or share of commercial space reflect inherent relative nearness of people, stores, etc. • The mobility-for-accessibility perspective implies a largely utilitarian perspective – we travel to derive accessibility (e.g., “travel is a derived demand”) • But, travel is not always a “means” to an “end,” – “travel liking” (due to adventure, variety, independence desires, etc.) and not just for leisure trips, but for routine trips and not just for auto use (see Ory and Mokhtarian, 2005) – Extra travel as a means of “information gain” (i.e., better information on products, space, etc.) (Arentze and Timmermans, 2005) • Travel’s role in social class formation – E.g., Vasconcellos (1997) details the role of the car in the “making of the middle class.” • And, of course, in combination: e.g., in integrated LUT models Basics of Mobility Demand Relevant Basic Characteristics • Purposes: – Work, Shopping, Social, Recreational, Business, School, Others • Origin: – e.g., Home-based work, Home-based school, etc. nonhome-based shopping, etc. • Stage: – e.g., Stage 1, 2, etc. • Mode: – car, bus, rail, etc. • Time of Day: – e.g., AM-Peak, Off-peak, etc. • Tour: – combination of trips taken between “anchors” (activity-based modeling); multiple activities in a single tour = “trip chaining” Sources of Data • Fundamental source – Household Origin-Destination Survey – Should be calculated for a given Metropolitan Area • National-level Surveys – E.g., Censuses – In US: NPTS: 1969, 1977, 1983, 1990, 1995, 2001 (NHTS) – 2001 NHTS: 26,000 households, national-level, 24­ hour “travel day” diary, plus 28-day “Travel Period” for long-distance travel (see nhts.ornl.gov) • Distance, Time, other? 3 Santiago (2001) How do people get around? US MSAs 2000: Work Trip Mode Share Walk Carpool Saturday Summer Weekday 40 Mode Share All Trips Public Transport Weekday Sunday 45 35 30 25 20 15 10 5 k er s O th W al C Source: Hanson, 2004. Bi ke ct iv o xi ol e ro Ta s M et Bu Au to 0 Drive Solo Range of “Developing World” Cities Image removed for copyright purposes. See Figure 9 in "mobility 2001." World Business Council for Sustainable Development, 2001, p.18. Why do we Travel: Trip Purposes? Other Comm ute Social http://www.wbcsd.org/web/projects/mobility/english_overview.pdf Shop Pers onal Source: U.S. DOE, 2004. Source: Various, see WBCSD, Table A-1. Travel Demand: Relevant Personal Choices • Activity choices – result in the number of tours and trips made by a person for a certain purpose • Destination choices • Mode choices – car, train, bus, tram, metro, etc. • Time-of-day choices • Route choices Travel Demand (Activity Choice + Destination Choice + Mode Choice) = f (Socio-Economics/Demographics, Communication Patterns/Time Routines, Travel Costs (generalized), Modal Availability, Land Use Patterns) 4 Major Socio-Economic and Demographic Drivers The Stylized “S-Curve" • Household Income – Car ownership Auto Ownership • although elasticities different at different income levels: e.g., S-curve – Longer and more trips – Higher demand for speed • higher value of time Threshold income for auto ownership Personal Income National Motorization Rate Where’s the S-Curve? Household-Based S-Curves… Autos and Motorized Two-Wheelers in Chennai, India (1993) Figure removed for copyright purposes. See Willoughby, Christopher. "Managing Motorization." World Bank Report TWU-42, April 2000, p. 8. http://www.worldbank.org/html/fpd/transport/publicat/twu_42.pdf Percent of Households 70 Cars 60 Two Wheelers 50 40 30 20 10 0 <20 20 41 62 83 104 125 146 167 188 209 >209 Monthly Income (US$) Source: Willoughby, 2000, p. 8. Source: RITES, 1995 U.S. Household Travel: Sated Demand? 0.900 12,000 0.800 0.700 0.600 8,000 0.500 6,000 0.400 0.300 4,000 2,000 Vehicle-miles per capita 0.200 Vehicles per capita 0.100 19 50 19 60 19 70 19 80 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 0 0.000 Trips/Capita/Day 10,000 Vehicles per Capita Vehicle Miles Per Capita Income and the Demand for Trips Personal Business Work/ School Social Social Shopping Shopping Shopping Work/ School Work/ School Work/ School Income Source: U.S. DOE, 2004. 5 Income and the Demand for Trips Income and Mode Share - Santiago 80 3 % of All Trips 2.5 2 1.5 Walking 70 Auto 60 Public Transport 50 40 30 20 1 10 0.5 0 0 <$230 $230$455 $455$910 $910$1640 $1640$3300 >$3300 < 17 $1 17 $1 8 20 -$ 08 $2 Income and Mode Share - São Paulo 94 $4 0 75 -$ 50 $7 60 11 -$ 0 16 $1 -$ 65 28 > 5 86 $2 Income, Motorization Rate & Mode Share – Santiago Walking 60% Auto Public Transport Auto Mode Share 60% % of All Trips 4 49 -$ Source: SECTRA, 1991. Source: Companhia do Metropolitano de São Paulo, 1999. 70% 16 $3 Monthly Incom e (US$ 1991) Average Household Income (US$1997) 80% 16 -3 50% 40% 30% 20% 50% 400 Auto Mode Share Veh/1000 350 300 40% 250 30% 200 150 20% 100 10% 50 0% 0 10% Vehicles per 1000 Pop. Trips/Person/Day São Paulo 1997 0% <230 230-455 455-910 910-1640 1640-3300 >3300 Monthly Income (US$1997) Source: Companhia do Metropolitano de São Paulo, 1999. Source: SECTRA, 1991. The Theory of Constant Travel Budgets What Does Schafer Say About This? Hours/Day Spent Traveling United States (1995) Netherlands (1995) Norway (1992) Australia (1986) United States (1977) Netherlands (1985) Norway (1985) Switzerland (1994) Switzerland (1984) Great Britain (1995) W. Germany (1976) W. Germany (1989) Japan (1992) Great Britain (1976) Singapore (1991) Singapore (1991) Chana Villages (1988) Tanzania VIII. (1986) 0 1 Travel Cost, Percent of Disposable Income 20 Source: Updated data based on Schafer (1998). 10 Kilometers Traveled/Person/Day 20 0 40 80 Figure by MIT OCW. 6 How Would Schafer Respond? Passenger Travel Growth, Past 50 Years. 12,000 0.900 WED 100,000 EEU PAO CPA 10,000 SAS LAM MEA AFB 1,000 0.800 10,000 0.700 8,000 0.500 6,000 0.400 4,000 2,000 PAS World 0.600 0.300 Vehicle-miles per capita 0.200 Vehicles per capita 0.100 0 0.000 19 50 19 60 19 70 19 80 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 Passenger km/Capita FSU Vehicle Miles Per Capita NAM Vehicles per Capita Implies: Increased Income=Increased Travel 100 100 1,000 10,000 100,000 Source: U.S. DOE, 2004. GDP/Capita, SUS(1985) Source: Updated data based on Schafer (1998). Figure by MIT OCW. But, be careful with National to Global Level Averages… 7