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Introduction to Travel Demand/Behavior, or What about the People in Transportation?

Introduction to Travel Demand/Behavior, or What about the People in Transportation?. Prof. Patricia L. Mokhtarian, Dept. of Civil & Environmental Engineering & Institute of Transportation Studies University of California, Davis plmokhtarian@ucdavis.edu www.its.ucdavis.edu/telecom/. Premise.

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Introduction to Travel Demand/Behavior, or What about the People in Transportation?

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  1. Introduction to Travel Demand/Behavior, orWhat about the People in Transportation? Prof. Patricia L. Mokhtarian, Dept. of Civil & Environmental Engineering & Institute of Transportation Studies University of California, Davis plmokhtarian@ucdavis.edu www.its.ucdavis.edu/telecom/

  2. Premise An understanding of individuals’ travel behavior is important to: • forecasting future travel demand • evaluating the effectiveness of policies • predicting the response to new technologies or services • anticipating possible unintended consequences

  3. Overview • “Demand” versus “behavior” • Why do people travel? • Trends in travel demand • Modeling travel demand/behavior • Policy measures and travel behavior • Summary and conclusions

  4. Demand Aggregate Forecast TRB: ADB40, Transportation Demand Forecasting Behavior Disaggregate Explain TRB: ADB10, Traveler Behavior and Values “Demand” v. “Behavior” Both deal with people’s travel choices/patterns/trends

  5. Why do People Travel? • (Why did the chicken cross the road?) • Duh – to get where they want to be??? • Hence, the truism that “Travel is a derived demand” – i.e. the demand for travel is derived from the demand for spatially-separated activities • Corollary: Travel is a disutility, that people try to minimize

  6. Assumed Implications (1) • Saved travel time is a benefit, hence a basis for valuing transportation improvements • THE largest benefit component in most cost-benefit analyses • We can reduce travel by… • ... making it more expensive • congestion pricing, fuel taxes, parking pricing

  7. Assumed Implications (2) • We can reduce travel by… • … bringing activities closer together • increasing density and mixture of land uses • … using ICT to conduct the activity remotely • telecommuting, -conferencing, -shopping, -education, -medicine, -justice • We can better forecast travel by under-standing people’s activity engagement – the so-called “activity-based approach” to modeling travel demand

  8. But is that the only reason people travel -- to get somewhere in particular?

  9. Why Would Travel be Intrinsically Desirable? • Escape • Exercise, physical/mental therapy • Curiosity, variety-, adventure-seeking; conquest • Sensation of speed or even just movement • Exposure to the environment, information • Enjoyment of a route, not just a destination • Ability to control movement skillfully • Symbolic value (status, independence) • Buffer between activities, synergy with multiple activities

  10. Assertions • Those characteristics apply not only to undirected (recreational) travel, but to directed travel as well • varying by mode, purpose, individual, circumstance • Even if “derived”, travel can simultaneously be intrinsically valued • in which case, people will be less inclined to reduce it than an evaluation of its “derived” nature alone would suggest

  11. Trends in Travel Demand

  12. U.S. Trends, 1950-2006 (1950 = 100)

  13. http://www.bts.gov/publications/bts_transportation_trends_in_focus/2010_04_01/html/figure_03.html, accessed 9/30/2011

  14. http://www.bts.gov/publications/bts_transportation_trends_in_focus/2010_04_01/html/figure_04.html, accessed 9/30/2011

  15. http://www.bts.gov/publications/special_reports_and_issue_briefs/special_report/2007_10_03/html/figure_01.html, accessed 9/30/2011

  16. Global Changes, 1960-1990 NAM: N. America LAM: Latin America WEU: W. Europe EEU: E. Europe FSU: Former Soviet Union MEA: Middle East and North Africa AFR: Sub-Saharan Africa CPA: Centrally Planned Asia and China SAS: South Asia PAS: Other Pacific Asia PAO: Other Pacific OECD Motorized mobility (pkm) per capita, 1960 and 1990. Source: Schafer, 1998

  17. pkm by mode, 1970-2001 (EU-15) Source: European Commission, 2003

  18. European Private Auto Passenger Travel, 1990-2008

  19. Ave. Annual Growth Rate of Cars and Their Use, 1970-90 Source: USDOT, 1997, Figure 10-2, p. 231

  20. Auto Travel, 1970-2001 (EU-15) Source: European Commission, 2003

  21. Intra-European Airline Passenger-km, 1970-2001 Data source: Eurostat/DGTREN. Source of figure: CNT, 2004

  22. International Airline Passengers, 1993-2001 Data source: Eurostat. Source of figure: CNT, 2004

  23. Mobility as a Function of GDP NAM: N. America LAM: Latin America WEU: W. Europe EEU: E. Europe FSU: Former Soviet Union MEA: Middle East and North Africa AFR: Sub-Saharan Africa CPA: Centrally Planned Asia and China SAS: South Asia PAS: Other Pacific Asia PAO: Other Pacific OECD Motorized mobility (car, bus, rail, and aircraft) per capita by world region vs GDP per capita, between 1960 and 1990. Source: Schafer, 1998

  24. Car Ownership v. GDP SAS: South Asia PAS: Other Pacific Asia CPA: Centrally Planned Asia and China Estimated motorization rates for CPA, PAS and SAS, compared with the observed rise in motorization in several countries. Source of historical data: United Nations, 1960; United Nations, 1993a and IRF, various years. Source for figure: Schafer and Victor, 2000

  25. Projected Mobility, 2050 Historical and estimated future total global mobility by mode in 1960, 1990, 2020 and 2050. Source: Schafer and Victor, 2000

  26. Modeling Travel Demand/Behavior

  27. Regional Travel Demand Forecasting (RTDF) (1) • Or, the Urban Transportation Planning System (UTPS) • The workhorse of metropolitan area planners (ECI 251) • forecast demand • evaluate alternatives • Calibrated with data from a large-scale travel/activity diary survey (TTP 200)

  28. Regional Travel Demand Forecasting (RTDF) (2) • The model contains 4 stages or submodels, corresponding to a set of choices that individuals are assumed to make: • whether to travel (trip generation) • where to travel (trip distribution) • by what means (mode) to travel (mode choice) • by what route (route assignment)

  29. Regional Travel Demand Forecasting (RTDF) (3) • Example analysis tools used: • cross-classification, regression (trip generation) • gravity model (trip distribution) • probabilistic discrete choice – ECI 254 (mode choice) • network optimization – ECI 257 (route assignment)

  30. Other Aggregate Demand Models • Auto ownership • Nationwide vehicle-miles traveled (VMT) • Travel time – is there a “travel time budget”? • Fuel consumption • Air travel demand • TOOLS: • Regression • Time series • Structural equations modeling

  31. Disaggregate Behavioral Models/Tools • ANOVA, regression • Discrete choice(residential location, auto ownership, # of trips, destination, mode, route, combinations)

  32. Discrete Choices of Work/Commute Engagement/Location • Work engagement – work frequency – commute frequency

  33. Discrete Choices of Work/Commute Engagement/Location • Work engagement – commute engagement – type of partial commute

  34. Disaggregate Behavioral Models/Tools • ANOVA, regression • Discrete choice(resid. loc., auto own., # of trips, destination, mode, route, combinations) • Hazard models(activity durations, how long a vehicle is owned, time till accident, length of tele-commuting engagement) • Factor analysis– TTP 200 (attitude/opinion measurement) • Structural equations modeling(relationships among attitudes, residential location, and travel behavior; relationships between telecom and travel)

  35. Relative Desired Mobility Mobility Constraints General Travel Attitudes Travel Liking Personality & Lifestyle Demographics Objective Mobility Subjective Mobility Structural Model of Mobility Preferences/Behavior

  36. Structural Model of Telecom/ Travel Relationships Socio-demographics Economic Activity Transporta-tion System Infrastructure Travel Demand Telecommuni-cations Demand Telecommuni- cations System Infrastructure Telecommuni-cations Costs Travel Costs Land Use Endogenous Variable Category Exogenous Variable Category

  37. Socioeconomic & Demographic Traits Attitudes c d b e Residential Choice (BE) Travel Behavior a Relationships among Attitudes, Land Use, & Travel Behavior

  38. Policy Measures and Travel Behavior

  39. When you think about it, virtually ALL policies are intended to affect behavior, whether they are ... • … supply-oriented, or • demand-oriented

  40. Supply-oriented Policies • Expand physical infrastructure • Does this in itself stimulate the realization of latent demand? • More effectively manage existing supply (Transportation Supply Management, TSM) • Increase supply or reduce costs • to underserved populations • of using non-auto modes

  41. Demand-oriented Policies • Generally intended to reduce demand, by • changing the cost signals (internalizing externalities, i.e. raising costs!) • changing land use planning to bring activities closer together • promoting ICT substitution • Collectively referred to as Transportation Demand Management (TDM) strategies

  42. Summary • People travel for many reasons besides the obvious one; it is a fundamental human need • Worldwide trends are toward more travel, not just due to population growth, but per capita • It is a challenge to balance the human need for mobility against the need for sustainability • We need to better understand the need to travel for its own sake, and reasons behind various travel decisions • Implications for modeling, evaluation, policy

  43. Discussion Questions • DOES virtual mobility reduce the need for real mobility? • How can we balance the human need for mobility against the need for sustainability? • Should policymakers try harder to discourage “unnecessary” travel? What are the most effective ways of doing so? • Can people express the extent to which they travel “for its own sake”?

  44. Other Questions? plmokhtarian@ucdavis.edu www.its.ucdavis.edu/telecom/ Slide borrowed from David Ory

  45. Selected References CNT (Conseil National des Transports, Observatory on Transport Policies and Strategies in Europe) (2004) Bulletin Transports/Europe No. 11. Available at www.cnt.fr. European Commission (2003) European Union Energy & Transport in Figures. Directorate-General for Energy and Transport. Handy, Susan (2002) Accessibility- vs. mobility-enhancing strategies for addressing automobile dependence in the US. Prepared for the European Council of Ministers of Transport Roundtable 124, on Transport and Spatial Policies, November 7-8, Paris. Houseman, Gerald (1979) The Right of Mobility. Port Washington, NY: Kennikat Press. Mokhtarian, Patricia L. & Cynthia Chen (2004) TTB or not TTB, that is the question: A review and analysis of the empirical literature on travel time (and money) budgets. Transportation Research A38(9-10), 643-675. Mokhtarian, Patricia L. & Ilan Salomon (2001) How derived is the demand for travel? Some conceptual and measurement considerations. Transportation Research A35, 695-719. Schafer, Andreas (1998) The global demand for motorized mobility. Transportation Research A32(6), 455-477. Schafer, Andreas and David G. Victor (2000) The future mobility of the world population. Transportation Research A34(3), 171-205. U. S. Department of Transportation (1997) Transportation Statistics Annual Report 1997: Mobility and Access. Washington, DC: USDOT Bureau of Transportation Statistics. Available at http://www.bts.gov/publications/transportation_statistics_annual_report/1997/pdf/report.pdf.

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