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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
CPA: Centrally Planned Asia and China
SAS: South Asia
PAS: Other Pacific 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
PAS: Other Pacific Asia
CPA: Centrally Planned Asia and China
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)
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)
Regional Travel Demand Regional Travel Demand Forecasting (RTDF) (2)Forecasting (RTDF) (2)
Regional Travel Demand Regional Travel Demand Forecasting (RTDF) (2)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 Regional Travel Demand Forecasting (RTDF) (3)Forecasting (RTDF) (3)
Regional Travel Demand Regional Travel Demand Forecasting (RTDF) (3)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 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
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
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
Discrete Choices of Work/Commute Discrete Choices of Work/Commute Engagement/LocationEngagement/Location
Discrete Choices of Work/Commute Discrete Choices of Work/Commute Engagement/LocationEngagement/Location
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
Disaggregate Behavioral Disaggregate Behavioral Models/ToolsModels/Tools
Disaggregate Behavioral Disaggregate Behavioral Models/ToolsModels/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)
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?
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.