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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

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/


Premise

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


Overview

Overview

  • “Demand” versus “behavior”

  • Why do people travel?

  • Trends in travel demand

  • Modeling travel demand/behavior

  • Policy measures and travel behavior

  • Summary and conclusions


Demand v behavior

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


Why do people travel

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


Assumed implications 1

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


Assumed implications 2

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


But is that the only reason people travel to get somewhere in particular

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


Why would travel be intrinsically desirable

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


Assertion

Assertion

  • Those characteristics apply not only to undirected (recreational) travel, but to directed travel as well

    • varying by mode, purpose, individual, circumstance


Trends in travel demand

Trends in Travel Demand


Global changes 1960 1990

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


Pkm by mode 1970 2001 eu 15

pkm by mode, 1970-2001 (EU-15)

Source: European Commission, 2003


Ave annual growth rate of cars and their use 1970 90

Ave. Annual Growth Rate of Cars and Their Use, 1970-90

Source: USDOT, 1997, Figure 10-2, p. 231


Auto travel 1970 2001 eu 15

Auto Travel, 1970-2001 (EU-15)

Source: European Commission, 2003


Intra european airline passenger km 1970 2001

Intra-European Airline Passenger-km, 1970-2001

Data source: Eurostat/DGTREN. Source of figure: CNT, 2004


International airline passengers 1993 2001

International Airline Passengers, 1993-2001

Data source: Eurostat. Source of figure: CNT, 2004


Mobility as a function of gdp

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


Car ownership v gdp

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


Projected mobility 2050

Projected Mobility, 2050

Historical and estimated future total global mobility by mode in 1960, 1990, 2020 and 2050.

Source: Schafer and Victor, 2000


Modeling travel demand behavior

Modeling Travel Demand/Behavior


Regional travel demand forecasting rtdf 1

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)


Regional travel demand forecasting rtdf 2

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)


Regional travel demand forecasting rtdf 3

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)


Other aggregate demand models

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


Disaggregate behavioral models tools

Disaggregate Behavioral Models/Tools

  • ANOVA, regression

  • Discrete choice (residential location, auto ownership, # of trips, destination, mode, route, combinations)


Discrete choices of work commute engagement location

Discrete Choices of Work/Commute Engagement/Location

  • Work engagement – work frequency – commute frequency


Discrete choices of work commute engagement location1

Discrete Choices of Work/Commute Engagement/Location

  • Work engagement – commute engagement – type of partial commute


Disaggregate behavioral models tools1

Disaggregate Behavioral Models/Tools

  • ANOVA, regression

  • Discrete choice (resid. loc., auto own., # of trips, destination, mode, route, combinations)

  • Hazard models (activity durations, time till accident, length of telecommuting engagement)

  • Factor analysis – TTP 200 (attitude/opinion measurement)

  • Structural equations modeling (relationships among attitudes, residential location, and travel behavior; relationships between telecom and travel)


Structural model of mobility preferences behavior

Relative Desired Mobility

Mobility

Constraints

General Travel

Attitudes

Travel Liking

Personality

& Lifestyle

Demographics

Objective Mobility

Subjective Mobility

Structural Model of Mobility Preferences/Behavior


Structural model of telecom travel relationships

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


Policy measures and travel behavior

Policy Measures and Travel Behavior


When you think about it virtually all policies are intended to affect behavior whether they are

When you think about it, virtually ALL policies are intended to affect behavior, whether they are ...

  • … supply-oriented, or

  • demand-oriented


Supply oriented policies

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


Demand oriented policies

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


Summary

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


Discussion questions

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”?


Other questions

Other Questions?

plmokhtarian@ucdavis.edu

www.its.ucdavis.edu/telecom/

Slide borrowed from David Ory


Selected references

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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