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: 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” v. “Behavior” Demand
Aggregate
Forecast
TRB: ADB40, Transportation Demand Forecasting Behavior
Disaggregate
Explain
TRB: ADB10, Traveler Behavior and Values 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 Motorized mobility (pkm) per capita, 1960 and 1990.
Source: Schafer, 1998 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
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
Per Capita km, 2001: Per Capita km, 2001 Source: European Commission, 2003
Mobility as a Function of GDP: Mobility as a Function of GDP Motorized mobility (car, bus, rail, and aircraft) per capita by world region
vs GDP per capita, between 1960 and 1990. Source: Schafer, 1998 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
Car Ownership v. GDP: Car Ownership v. GDP 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 SAS: South Asia
PAS: Other Pacific Asia
CPA: Centrally Planned Asia and China
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
Projected Mobility, 2050: Projected Mobility, 2050 Per-capita and total mobility for 11 regions (and share of global total) in 1960, 1990, 2020, and 2050
for the reference scenario.
NAM: N. America AFR: Sub-Saharan Africa
LAM: Latin America CPA: Centrally Planned Asia and China
WEU: W. Europe SAS: South Asia
EEU: E. Europe PAS: Other Pacific Asia
FSU: Former Soviet Union PAO: Other Pacific OECD
MEA: Middle East and North Africa 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/Location: Discrete Choices of Work/Commute Engagement/Location
Work engagement – commute engagement – type of partial commute
Disaggregate Behavioral Models/Tools: 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: Structural Model of Mobility Preferences/Behavior
Structural Model of Telecom/ Travel Relationships: Endogenous Variable Category Travel Demand Exogenous Variable Category Telecommuni-cations
Demand Transporta-tion System
Infrastructure Telecommuni- cations System
Infrastructure
Travel Costs Telecommuni-cations Costs
Economic
Activity Structural Model of Telecom/ Travel Relationships
Land Use
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 A 38(9-10), 643-675.
Mokhtarian, Patricia L. & Ilan Salomon (2001) How derived is the demand for travel? Some conceptual and measurement considerations. Transportation Research A 35, 695-719.
Schafer, Andreas (1998) The global demand for motorized mobility. Transportation Research A 32(6), 455-477.
Schafer, Andreas and David G. Victor (2000) The future mobility of the world population. Transportation Research A 34(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.