Introduction to Travel Demand/Behavior, or What about the People in Transportation? Prof. Patricia L. Mokhtarian, Dept. of Civil & Environmental Engineering.

Introduction to Travel Introduction to Travel Demand/Behavior, orDemand/Behavior, or

What about the People in What about the People in Transportation?Transportation?

Introduction to Travel Introduction to Travel Demand/Behavior, orDemand/Behavior, or

What about the People in What about the People in Transportation?Transportation?

Prof. Patricia L. Mokhtarian,Dept. of Civil & Environmental Engineering

& Institute of Transportation StudiesUniversity of California, Davis

[email protected]/telecom/

PremisePremisePremisePremise

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

OverviewOverviewOverviewOverview

“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” v. “Behavior”Demand” v. “Behavior”

DemandDemand– Aggregate

– Forecast

– TRB: ADB40, Transportation Demand Forecasting

BehaviorBehavior– 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 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)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)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 But is that the only reason people travel -- to get somewhere in travel -- to get somewhere in

particular?particular?

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

particular?particular?

Why Would Travel be Why Would Travel be Intrinsically Desirable?Intrinsically Desirable?Why Would Travel be Why Would Travel be Intrinsically Desirable?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

AssertionsAssertionsAssertionsAssertions

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

Trends in Travel DemandTrends in Travel DemandTrends in Travel DemandTrends in Travel Demand

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

0

100

200

300

400

500

600

700

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Y1

0

2000

4000

6000

8000

10000

12000

Y2

VMT (cars+light trucks), Y1

Transit passengers, Y1

Airline domestic PMT, Y2

Airline international PMT, Y2

U.S. VMT 1990-2009

0

50

100

150

200

250

300

Oct-89 Jul-92 Apr-95 Jan-98 Oct-00 Jun-03 Mar-06 Dec-08

bil

lio

ns

Vehicle Miles Traveled Vehicle Miles Traveled - Seasonally Adjusted

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

U.S. VMT 2001-2009

0

50

100

150

200

250

300

Oct-00 Feb-02 Jun-03 Nov-04 Mar-06 Aug-07 Dec-08 May-10

bil

lio

ns

Vehicle Miles Traveled Vehicle Miles Traveled - Trend

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

U.S. VMT -- Percent Change Since 1970

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

1970 1975 1980 1985 1990 1995 2000 2005

Population Real Personal Income Passenger VMT

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

Global Changes, 1960-1990Global Changes, 1960-1990Global Changes, 1960-1990Global 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)pkm by mode, 1970-2001 (EU-15)pkm by mode, 1970-2001 (EU-15)

0

1000

2000

3000

4000

5000

6000

1970 1975 1980 1985 1990 1995 2000

1000

mio

pkm

Passenger Cars

Buses & Coaches

Tram + Metro

Railway

Air

Total

Source: European Commission, 2003

European Private Auto European Private Auto Passenger Travel, 1990-2008Passenger Travel, 1990-2008

European Private Auto European Private Auto Passenger Travel, 1990-2008Passenger Travel, 1990-2008

Ave. Annual Growth Rate of Ave. Annual Growth Rate of Cars and Their Use, 1970-90Cars and Their Use, 1970-90Ave. Annual Growth Rate of Ave. Annual Growth Rate of Cars and Their Use, 1970-90Cars 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)Auto Travel, 1970-2001 (EU-15)Auto Travel, 1970-2001 (EU-15)

0

100

200

300

400

500

600

700

800

1970 1975 1980 1985 1990 1995 2000

1000

mio

pkm

B

DK

D

EL

E

F

IRL

I

L

NL

A

P

FIN

S

UK

Source: European Commission, 2003

Intra-European Airline Intra-European Airline Passenger-km, 1970-2001Passenger-km, 1970-2001

Intra-European Airline Intra-European Airline Passenger-km, 1970-2001Passenger-km, 1970-2001

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

International Airline Passengers, International Airline Passengers, 1993-20011993-2001

International Airline Passengers, International Airline Passengers, 1993-20011993-2001

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

Mobility as a Function of GDPMobility as a Function of GDPMobility as a Function of GDPMobility 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


SAS: South Asia


PAO: Other Pacific OECD

Car Ownership v. GDPCar Ownership v. GDPCar Ownership v. GDPCar 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



Projected Mobility, 2050Projected Mobility, 2050Projected Mobility, 2050Projected Mobility, 2050

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

Modeling Travel Modeling Travel Demand/BehaviorDemand/BehaviorModeling Travel Modeling Travel

Demand/BehaviorDemand/Behavior

Regional Travel Demand Regional Travel Demand Forecasting (RTDF) (1)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)



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)



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 ModelsOther Aggregate Demand ModelsOther Aggregate Demand ModelsOther 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 Disaggregate Behavioral Models/ToolsModels/Tools


ANOVA, regression Discrete choice (residential location, auto ownership, #

of trips, destination, mode, route, combinations)

Discrete Choices of Work/Commute Discrete Choices of Work/Commute Engagement/LocationEngagement/Location


Work engagement – work frequency – commute frequency

choice

work

Part-time worker

Non-worker

Compressed-schedule worker

full-time

Fully-commuting

worker

Home-based worker

Telecommuter



Work engagement – commute engagement – type of partial commute

Compressed-schedule worker

partial commuter

Fully-commuting

worker

Home-based worker

Telecommuter

choice

work

Part-time worker

Non-worker



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)

Structural Model of Mobility Structural Model of Mobility Preferences/BehaviorPreferences/Behavior

Structural Model of Mobility Structural Model of Mobility Preferences/BehaviorPreferences/Behavior

General TravelAttitudes

Personality & Lifestyle

Demographics

Objective Mobility

Relative Desired Mobility

Travel Liking

Subjective Mobility

MobilityConstraints

Endogenous Variable Category

Socio-demographics

Socio-demographics

Travel Demand

Travel Demand

Exogenous Variable Category

Telecommuni-cations

Demand

Telecommuni-cations

Demand

Transporta-tion System

Infrastructure

Transporta-tion System

Infrastructure

Telecommuni- cations System

Infrastructure

Telecommuni- cations System

Infrastructure

Travel CostsTravel CostsTelecommuni-cations Costs

Telecommuni-cations Costs

Economic Activity

Economic Activity

Structural Model of Structural Model of Telecom/ Travel Telecom/ Travel

RelationshipsRelationships

Structural Model of Structural Model of Telecom/ Travel Telecom/ Travel

RelationshipsRelationships

Land Use

Land Use

AttitudesSocioeconomic &Demographic Traits

ResidentialChoice (BE)

TravelBehaviora

c

b e

d

Relationships among Relationships among Attitudes, Land Use, & Attitudes, Land Use, &

Travel BehaviorTravel Behavior

Relationships among Relationships among Attitudes, Land Use, & Attitudes, Land Use, &

Travel BehaviorTravel Behavior

Policy Measures and Travel Policy Measures and Travel BehaviorBehavior

Policy Measures and Travel Policy Measures and Travel BehaviorBehavior

When you think about it, When you think about it, virtually ALL policies are virtually ALL policies are

intended to affect behavior, intended to affect behavior, whether they are ...whether they are ...

When you think about it, When you think about it, virtually ALL policies are virtually ALL policies are

intended to affect behavior, intended to affect behavior, whether they are ...whether they are ...

… supply-oriented, or demand-oriented

Supply-oriented PoliciesSupply-oriented PoliciesSupply-oriented PoliciesSupply-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 PoliciesDemand-oriented PoliciesDemand-oriented PoliciesDemand-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

SummarySummarySummarySummary 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 QuestionsDiscussion QuestionsDiscussion QuestionsDiscussion 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 Other Questions?Questions?

Other Other Questions?Questions?

[email protected]

www.its.ucdavis.edu/telecom/Slide borrowed from David Ory

Selected ReferencesSelected ReferencesSelected ReferencesSelected ReferencesCNT (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.

Introduction to Travel Demand/Behavior, or What about the People in Transportation? Prof. Patricia L. Mokhtarian, Dept. of Civil & Environmental Engineering.

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