Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Synthesis of Traveler Choice Research: 

Improving Modeling Accuracy for Better 

Transportation Decisionmaking

Publication No. FHWA-HRT-13-022 August 2013
FOREWORD

Travelers’ choices are central to the performance of a transportation system, but little is known about 

what influences such choices or the impact they have on system performance. When selecting a 

transportation management strategy, a transportation management center operator must understand 

and anticipate how travelers will respond: Will they stay on the same routes or divert? Will they 

decide to walk, bike, or take a bus or train instead of driving? Will they leave earlier or later?

The operator must know the potential benefits of alternative overall strategies (e.g., variable pricing

or information on dynamic message signs) as well as how to handle day-to-day operations by 

implementing strategies to provide effective responses to particular events. The operator must also

account for non-network, predisposing factors that influence travelers’ choices. Such factors, 

including land use, population density, and walkability, are generally out of the control of the 

network manager, and their influence may not be intuitively obvious.

The project “Analysis of Traffic Network and Non-Network Impacts Upon Traveler Choice” 

addresses the current state of the practice, advances understanding, and identifies gaps in knowledge

regarding traveler choices. This synthesis report documents the project’s first major activity: an 

assessment of current research and practices in traveler choice. It will be a resource for both traveler 

choice researchers and organizations considering transportation management strategies that influence

traveler choice. This report also lays the foundation for the project’s next step, the development 

of traveler choice models that can be incorporated into existing transportation analysis tools. 

Joseph I. Peters

Director, Office of Operations

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use 

of the information contained in this document. This report does not constitute a standard, 

specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-13-022

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Synthesis of Traveler Choice Research: Improving Modeling Accuracy for 

Better Transportation Decisionmaking

5. Report Date

August 2013

6. Performing Organization Code

7. Author(s)

Hani S. Mahmassani, Frank Koppelman, Charlotte Frei, Andreas Frei, and 

Robert Haas

8. Performing Organization Report No.

9. Performing Organization Name and Address

SAIC

1710 SAIC Drive 

McLean, VA 22102

Northwestern University

The Transportation Center

600 Foster Street 

Evanston, IL 60208-4055

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-06-D-00005

12. Sponsoring Agency Name and Address

Office of Operations

Federal Highway Administration

6300 Georgetown Pike 

McLean, VA 22101-2296 

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Technical Representatives (COTRs) were Taylor W.P. Lochrane and David C.Y. Yang.

16. Abstract

Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been 

immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density 

together with externalities in these areas. The field has changed from supply-oriented planning to incorporating and 

managing demand. As such, methods from a variety of disciplines have been borrowed and extended to explain human 

behavior and interaction. Many experts have called for better data collection and methods of analysis across a number 

of time horizons, that is, integrated supply and demand models that capture travel behavior over time and space. A 

new paradigm may be called for to address the present challenges of model integration; user preferences, heterogeneity,

and endogeneity; habitual behavior; and human socializing. This report provides a synthesis of the state of knowledge 

in travel behavior research and identifies gaps in existing data, methods, and practices that must be filled to meet the 

analysis needs of an emerging class of supply- and demand-side interventions that seek to leverage the opportunities of 

real-time information.

17. Key Words

Travel behavior, Active Transportation Demand Management (ATDM), 

Managing Travel Demand (MTD), Integrated Corridor Management 

(ICM), Active Traffic Management (ATM), User behavior response

18. Distribution Statement

No restrictions. This document is available 

to the public through NTIS:

National Technical Information Service

Springfield, VA 22161

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

55

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius o

C 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit o

F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS

OBJECTIVES, MOTIVATION, AND STRUCTURE...............................................................1

SCOPE AND CONCEPTUAL FRAMEWORK.........................................................................3

BACKGROUND ......................................................................................................................3

SCOPE DELINEATION.........................................................................................................3

CONCEPTUAL FRAMEWORK...........................................................................................4

TRAVEL BEHAVIOR STUDIES REVIEW ..............................................................................7

DAY-TO-DAY AND WITHIN-DAY BEHAVIOR CHANGES..........................................7

Effect of Tolling and Other Costs on Mobility Decisions...................................................8

Walk Quality on Day-to-Day Travel Behavior and Patterns...............................................9

BEHAVIORAL PATTERN CHANGES .............................................................................10

Sociodemographics and Household Composition .............................................................10

Effect of Travel Demand Management Measures and Parking Pricing on Mode Choice 11

Learning, Experience, and Inertia......................................................................................11

LIFESTYLE- AND MOBILITY-BASED BEHAVIOR CHANGES................................12

Effect of Transit-Oriented Development/Density on Behavior Patterns and 

Long-Term Choices...........................................................................................................12

Residential Self-Selection and Vehicle Ownership ...........................................................13

BEHAVIORAL MECHANISMS .........................................................................................14

Household Interactions ......................................................................................................15

Social Networks.................................................................................................................17

TRAVEL BEHAVIOR MODELS REVIEW............................................................................19

ACTIVITY-BASED MODELS.............................................................................................19

Utility-Based Econometric Models....................................................................................19

Rule-Based Computational Process Models......................................................................21

Agent-Based Modeling Systems........................................................................................22

MODEL INTEGRATION.....................................................................................................22

TRAVEL BEHAVIOR DATA REVIEW..................................................................................25

STANDARD SOURCES .......................................................................................................25

TRAVEL DIARIES ...............................................................................................................25

STATED PREFERENCE AND REVEALED PREFERENCE ........................................26

EMERGING POTENTIAL IN DATA COLLECTION ....................................................27

GROWING ROLE OF EXPERIMENTS AND GAMING METHODS ..........................28

CONCLUSIONS, RECOMMENDATIONS, AND FUTURE RESEARCH..........................31

REFERENCES.............................................................................................................................33

BIBLIOGRAPHY........................................................................................................................45
iv

LIST OF FIGURES

Figure 1. Chart. Conceptual Framework..........................................................................................5

Figure 2. Equation. Generalized Indifference Band Framework...................................................20

Figure 3. Equation. Random Utility Formulation..........................................................................21

Figure 4. Equation. Joint Versus Single Utility Threshold............................................................21

Figure 5. Equation. Weighted Sum of Joint Versus Single Utility Threshold...............................21

LIST OF TABLES

Table 1. Recent and Continuous National Travel Surveys............................................................25
v

LIST OF ACRONYMS

ATDM Active Transportation and Demand Management 

ATIS Advanced traveler information systems

GPS Global Positioning System

HOV High-occupancy vehicle

ICM Integrated Corridor Management

IT Information technology

MATSim Multi-Agent Transport Simulation Toolkit

MTD Managing Travel Demand 

RP Revealed preference

SOV Single-occupancy vehicle

SP Stated preference

TCRP Transit Cooperative Research Program

TRANSIMS Transportation Analysis and Simulation System

VMT Vehicle miles traveled

1

OBJECTIVES, MOTIVATION, AND STRUCTURE

The goal of this report is to organize the existing knowledge, the state of the art, and the current 

state of practice for transport modeling into a framework to give an overview of travel behavior 

research with particular relevance to traveler choice dimensions impacted through programs such as

Active Transportation and Demand Management (ATDM), Managing Travel Demand (MTD), and

Integrated Corridor Management (ICM), including weather-related dynamic system management. 

Because these programs entail a significant real-time operational element, choice models should 

enable understanding of users’ dynamic responses to information-rich environments and the 

interdependencies of users’ decisions over time. Some impacts of interventions applied as part of 

ATDM, MTD, and ICM projects are short-term, such as adjustments to a less congested route or 

departure time. Others appear over the long term, such as gradually shifting users’ behavior toward

more environmentally friendly or reliable trajectories with feedback information. Also of interest 

are the effects of non-network factors on users’ decisions and responses to operational interventions,

especially in terms of peak demand reduction and carbon-conscious behaviors.

Operational interventions offer substantial opportunity to optimize transportation networks, 

and they represent significant investment by agencies and contractors. Understanding user 

response at the facility, corridor, and network levels over short-, medium-, and long-term 

horizons is crucial to designing appropriate interventions. Without comprehensive knowledge 

of systems and user response, policies such as tolling and ramp metering may not lead to 

expected improvements or could cause externalities elsewhere in the network. The effects 

are complex, and such complexity demands a comprehensive look at how traveler behavior

is studied and modeled. Adding to the complexity, geographic and human heterogeneity 

mean shared results and insights may not lead to similar outcomes in different locations.

A comprehensive and easily transferable framework could overcome these issues to 

determine the structural causes and mechanisms of travel behavior. 

This report is structured into five sections. First, a conceptual organizing framework is established 

and discussed. The framework links operational interventions associated with dynamic demand 

and traffic management programs, including information dissemination, to corresponding traveler 

choice dimensions and to network and non-network factors affecting user response.

Second, a literature review is presented, mainly covering relevant knowledge along the established

conceptual framework. The review is composed of a general knowledge section, a discussion of 

models to capture travel behavior, and an overview of available relevant data sources. The review

addresses the question: “What do researchers know about traveler behavior?”

Third, travel behavior models are examined. Modelers must be careful, asking the appropriate 

questions and recognizing effects that are observed only through the modeling process. This 

section discusses the strengths and limitations of the variety of models available, pointing to 

areas of potential improvement. Improved model integration is needed to understand feedback 

loops and ensure model consistency.
2

Fourth, traveler behavior data sources are briefly reviewed. This section asks what data are needed 

based on existing and potential knowledge and modeling practices in travel behavior as well as

how relevant and important behaviors can be measured.

Finally, a summary is provided, examining conclusions about the current knowledge and 

opportunities to develop and advance traveler choice models, particularly in connection with 

the motivating applications. 
3

SCOPE AND CONCEPTUAL FRAMEWORK

This chapter discusses the report’s conceptual framework and defines categories to organize its

key elements. The framework’s core components are operational interventions, information 

dissemination, traveler choice dimensions, and network and non-network influencing factors.

BACKGROUND

For some time, transportation planning focused on meeting mobility needs by providing adequate

infrastructure. This supply-oriented planning neglected demand-oriented models in that the main 

purpose was to predict aggregate long-term demand for the strategic planning of the transport 

infrastructure, not necessarily taking full account of the impact of such plans on travel demand. 

The increasing costs of infrastructure and the spatial limitations in areas of high population density

together with the externalities in these areas have changed supply-oriented planning to incorporate

and manage demand. Based on this shift toward demand-oriented management, operational

interventions have emerged, including congestion pricing, which changes the service characteristics

to influence travel behavior, and dissemination of real-time information on the level of service. The 

interest in analyzing transport policies in terms of their impact has led to the use of disaggregate 

demand models, which seek to understand short-term effects of policies such as congestion pricing, 

telecommuting, and ride-sharing programs. The limitation of traditional trip-based travel models 

to capture the complex ways travelers respond to such policies has led to the development of 

behavior-oriented, activity-based models as well as the introduction of traveler response to 

current cost and service information.

Traditional trip-based static assignment models cannot cope with time-varying properties of 

traffic flow, which is essential in managing travel demand with timely and dynamic optimized 

interventions. The limitations of static assignment models and increasing computational capacities

have improved the supply side toward dynamic traffic assignment models, which are time-dependent

and able to model the buildup and dissipation of traffic congestion. Dynamic traffic assignment 

models are therefore able to accommodate the effects of intelligent transportation systems and 

system management interventions such as ramp meters, traffic lights, and congestion pricing. 

Although progress has been made on the demand side and the supply side, each area has progressed

rather independently of the other. Since travel behavior studies are related to many different fields,

most models do not capture all direct and indirect influences on travel behavior or all feedback 

effects from behavior decisions on travel volumes and service (which would capture the interaction

between the demand and supply models).

SCOPE DELINEATION

Traveler behavior research is a broad field. The synthesis presented in this report is not intended 

to be comprehensive of the whole field and all possible traveler choice dimensions, but rather, to 

focus on operational planning and management interventions influencing traveler behavior. The 

bounds at the operational management level have a more narrow scope than, for example, at a 

strategic level involving resource acquisition and network design. Nevertheless, long-term traveler 

decisions such as mode shifts, auto ownership, and location changes are of interest as activity 

assessments are becoming more realistic to model. On the intervention side, the focus is mainly on
4

ATDM, a strategy to operate technologies in a proactive way to address potential problems before 

they occur. ATDM covers MTD and ICM, including Dynamic Mobility Applications. This synthesis

also covers active traffic management on the supply side, since supply and demand management 

overlap in certain areas, such as information supply. Because most supply management interventions 

change the network’s level of service, the focus is on network factors influencing traveler behavior.

However, demand management interventions change non-network factors as well. The bounds 

of focus for non-network factors influencing traveler behavior are broad and not clearly defined.

Non-network factors range from weather, which is natural and easy to observe, to walkability, 

which can be designed for and is less straightforward to measure. Non-network factors are 

important, as they interact with network factors in decisionmaking and define the environment 

and attractiveness of choice alternatives. Traveler and vehicle characteristics also influence 

traveler choices. 

Taken together, these confounding forces and influences become difficult to separate. The 

comprehensive framework presented in this report attempts to conceptualize these person-network 

interactions over short-, medium-, and long-term time horizons. The framework seeks to capture 

the anticipated and actual effects of operational interventions on the supply and demand sides. 

CONCEPTUAL FRAMEWORK

Transport planning aims to describe, understand, and model the choices made by households and 

individuals during the execution of their daily lives, including the more or less frequent journeys 

outside their daily activity space.(1,2) Behavioral demand models feed supply and network models 

to assign traffic to the infrastructure. These models are in turn used to evaluate and optimize 

changes to the transport system undertaken by the owners of its various components, including 

reductions or expansions of road capacity through interventions, where demand management and 

supply management are involved, as well as policy changes. Travelers make decisions based on the 

characteristics of the system and their own perceptions. For example, as new information becomes 

available, travelers adjust their perception and adapt their travel behavior. Factors of the system and

en route decisions must be considered. Travelers also decide where they want to live, where their 

workplace is, and whether to own a car or buy monthly transit or toll passes. Travelers must decide 

how often and where their everyday and less frequent journeys take them, which mode of transport

to use (if multimodal trip alternatives are available), when to start trips, and what route to take. In 

decisionmaking, bounded rationality plays a substantial role: people make rational decisions based

on a limited amount of knowledge and assessment capacity, not necessarily fully informed or 

fully rational choices.

Operational interventions, supply and demand management, traveler choice dimensions, and 

factors affecting user response (either endogenous or exogenous) interact with each other, as

summarized in the conceptual framework in figure 1.
5

Figure 1. Chart. Conceptual Framework.

Household and individual behavior-change dimensions can be categorized on the basis of the 

time frame over which they take place, and hence, the level of analysis where a particular 

decision or group of decisions must be considered, as follows:

• Short-term, trip-level decisions take place within a day as well as from day to day. Trip-level 

decisions can be categorized further into the following types:(3)

o Pre-trip (strategic) high-level traveler choices take place before departure (i.e., 

trip-making decisions).

o En-route (tactical) high-level traveler choices take place during the trip (i.e., route 

modification).

• Medium-term decisions involve behavioral patterns such as activity chain planning and 

adjustments that take place over a longer period than hours or days.

• Long-term, lifestyle, and mobility decisions affecting vehicle holdings and location choices

take place over weeks, months, and years.
6

Operational intervention programs can be categorized by the interventions or controls with 

which they seek to improve system operations and performance by influencing underlying 

traveler choices.(4) They can target the supply side by modifying the network with traffic and 

infrastructure access controls (e.g., ramp metering), which affect behavior through the level 

of service as an influence factor. Alternatively, operational interventions may affect demand 

directly with pricing (e.g., congestion pricing). Demand and supply management overlap as 

information supply (e.g., variable message signs and earlier traveler time dissemination) 

targets both demand and supply indirectly through demand response. 

In addition to demand management, which influences household and individual behavior 

directly, there are further influencing factors, which can be divided into categories, as follows:

• Traveler.

o Traveler and household characteristics that affect traveler behavior.

o Vehicle characteristics that affect traveler behavior (e.g., type, dynamics).

• System.

o Network characteristics (e.g., connectivity, length of route, and roadway types) 

and segment elements that define roadway and transit path characteristics 

(e.g., ride quality, lanes, and frequency).

o Environment, events, states, or features of the network that affect traveler behavior

but do not originate from system control strategies (e.g., weather, walking paths, and 

other characteristics of transit service besides route configuration, such as headways).

It is important to note that behavior choices are denoted as behavior changes, as they are better 

represented as the outcome of an adjustment process of a current choice rather than as the outcome

of a choice process that does not recognize one’s current state. Also, the arrows in figure 1 not only

show the possible mappings of an explanatory variable on a possible outcome but also represent 

the perception of attributes and characteristics by the user in question.
7

TRAVEL BEHAVIOR STUDIES REVIEW

This section reviews studies of different levels of traveler decisionmaking as outlined in the 

conceptual framework, organizing travel behavior knowledge by decision horizon. At the withinday and day-to-day levels, route and departure time choices have been the primary focus. Travelers’

experiences from day to day influence their future decisions, and the line between these daily 

choices and a traveler’s behavioral pattern quickly blurs. For example, a traveler may eliminate 

public transportation from his or her choice set after a bad experience, even if the utility is otherwise 

perceived as quite high. Since mode choice is subject to available modes, it tends to be modeled 

and studied as a behavioral pattern in the time horizon of weeks or months. Finally, lifestyle and 

mobility choices reflect the self- or otherwise-imposed constraints to which travelers are subjected

(and choose) over longer time frames. Much work has been done within each area to understand 

how various factors influence these choices, but there is less understanding of the mechanisms 

that work to define these travel habits, patterns, and long-term constraints. These mechanisms 

and how they relate to different levels of traveler decisionmaking are discussed last.

DAY-TO-DAY AND WITHIN-DAY BEHAVIOR CHANGES

Jan et al. found that travelers habitually follow the same route for the same trip, but route variations 

increase with longer travel distances.(5) The dominant factors for route choice are travel time and 

distance.

(5–7) Significant research effort has been focused on the effects of route choice behavior 

under traffic information systems, the dynamic aspect of route choice behavior, and the 

relationships among route choice, departure time, and trip-chaining decisions.(8–10)

Traveler information influences route choice substantially. Abdel-Aty et al. studied route changes in 

Los Angeles, CA.

(7) Only a small share of the respondents (15 percent) reported using more than 

one route on their commute. Of that 15 percent, 34 percent said they changed routes after actually

seeing traffic conditions. Higher incomes and education levels predicted more route changes, 

perhaps reflecting schedule flexibility and arrival-time expectations for such workers.

Mahmassani and Herman performed a survey of commuters in Austin, TX, and yielded a binary 

logit model that relates route switching propensity to four types of factors: geographic and network 

condition variables, workplace characteristics, individual attributes, and use of information (radio

traffic reports).(11) They found that variables describing the characteristics of the commute itself 

had a dominant effect relative to workplace rules or individual characteristics. Information in the 

form of radio traffic reports also appeared to have a strong impact. Regular listeners to traffic 

information were more likely to switch routes. The only sociodemographic attribute significant 

in the model was age. 

In a similar experiment, Avineri and Prashker examined the impact of information on traveler 

learning, differentiated by travelers’ risk aversion.(12,13)The results suggest that when information

about travel times is provided, travelers do not always choose the route with the least expected time.

Giving static information to users increases traveler heterogeneity; in this case, individuals learned

more quickly to prefer either routes with less travel time or routes with less variability in travel time.

When examined at an aggregate level, this combination could be seen as a “non-learning effect” or 

no change. Furthermore, higher variation in travel times is associated with lower sensitivity to travel
8

time differences. Avineri and Prashker found in some cases that “increasing travel time variability

of a less attractive route could increase its perceived attractiveness.”

(12)

This underscores the need

for better models of learning and reinforced habits as an alternative to utility maximization.

Beyond these dimensions, only a couple of studies have addressed destination adjustment in 

response to real-time information for discretionary travel (shopping).

(14) The remainder of this 

section discusses the effects of other network and non-network factors that have been explored 

in more depth. 

Effect of Tolling and Other Costs on Mobility Decisions

Travel cost as part of demand management is a powerful tool to influence travel behavior. Hensher 

and King examined the influence of parking costs in the central business district, a park-and-ride 

facility with public transit access, and the related mode choice as well as destination choice 

(including the alternative to forgo the trip) in Sydney, Australia.(15)

Each of the participants was

required to consider six alternatives in a stated preference questionnaire. In 97 percent of the 

responses, cost was the most significant factor in location and mode choices. Similar results were 

found by Handy et al., who studied whether Americans drive by choice or necessity.(16)

The study

found that most drivers chose the car because of the costs and a lack of alternatives. However, 

studies of this nature in Europe may reflect stricter land use norms that have led to denser, more 

compact urban form and increased use of public transit but that also decrease costs for public 

transit operators and increase the cost of parking.

Congestion pricing of roadways presents a valuable opportunity to rationalize road networks by 

helping ensure that travelers pay for the delay costs they impose on others. A study of Seattle, WA,

travelers with Global Positioning System (GPS) vehicle units estimated that variable network 

pricing (to reflect the congestion impacts of different demand levels over space and time) would 

reduce regional vehicle miles traveled (VMT) by 12 percent and total travel time by 7 percent with 

a 6:1 benefit-cost ratio.

(17) Using GPS tolling meters, the study followed participants to establish 

a baseline tolling routine. Participants were then given a monetary travel budget sufficient to cover 

the cost of their routine for the duration of the study period, creating an incentive to reduce certain

forms of travel to save or make money. This policy approach is very similar to Kockelman and 

students’ credit-based congestion pricing policy proposal. However, VMT results differed in their 

network simulations of the Austin and Dallas-Ft. Worth regions of Texas, where marginal socialcost pricing of freeways for all links by time of day was consistently estimated to result in VMT 

savings of less than 10 percent.(18,19)

Saleh and Farrell investigated the influence of congestion pricing on the “peak-spreading” of 

departure time choice.(20) Results suggest that non-work activities and work schedule flexibility 

impact departure time choice for the trip to work. Furthermore, respondents were less willing to 

pay a toll to depart earlier than usual.

In a similar vein, Transit Cooperative Research Program (TCRP) Report 95 discusses a number of

elements that influence a traveler’s decision to use a high-occupancy vehicle (HOV) lane.(21) The 

report concludes that because so many urban, facility, and vehicle characteristics interact with one 

another it is difficult to delineate the effect of HOV lanes on travelers. However, the success of 
9

HOV lanes—both in terms of drivers served and benefits to the road network—is attributed to 

combinations of the following attributes:

• Urbanized population of 1.5 million or more.

• Orientation, preferably radial, to a city center, “focusing on major employment centers 

with preferably more than 100,000 jobs.”

• Geographic barriers.

• Congestion in general purpose lanes.

• Realistic potential for 25–30 buses per hour.

• Peak-hour travel time savings of preferably 1 min/mi or more or at least 5 min of total 

travel time.(21)

Walk Quality on Day-to-Day Travel Behavior and Patterns

Beyond information and pricing, the quality of the urban environment can influence route and 

activity timing decisions. Cervero and Kockelman examined many features of urban form that may

reduce auto dependence.(22)

Their gravity-based accessibility measure for access to commercial jobs

was found to have an elasticity of -0.27, suggesting neighborhood retail shops and pedestrianoriented design are more significant than residential densities in mode selection. Integrating aspects 

of pedestrian-oriented design such as four-way intersections and vertical mixing of land uses may

result in significant VMT reductions. For example, a 10 percent increase in the number of four-way 

intersections in a neighborhood was associated with an average reduction of 5.19 percent of person

miles traveled for non-work trips. A doubling of land use mix or variety is associated with a roughly 

11 percent increase in modes other than single-occupancy vehicle (SOV) for non-work travel. 

In addition to urban density, mixed land use and high-quality pedestrian-oriented urban design 

increase the use of public transit and non-motorized transport modes.

(23) Naess and Jensen found 

that, in general, car use increases with increasing distance from the city center.(24,25)

This could also

be an indicator of self-selection or endogeneity (a topic discussed in more depth in later sections).

Similarly, Cervero studied the impact of compact, mixed-use, pedestrian-friendly design on mode

choice.(26) The study quantified density and diversity and estimated the influence of each on mode

choice. The influences were significant but modest. Surprisingly, the most important influence 

factor for mode choice was the sidewalk ratio. In well-developed pedestrian areas, commuters 

were more likely to use public transit or join carpooling initiatives.

Information, pricing, and urban form influence day-to-day and within-day behaviors, but they are 

understood and applied over time such that they also influence travel patterns. These and other 

influences are discussed in the context of habits in the following sections.
10

BEHAVIORAL PATTERN CHANGES

Sociodemographics and Household Composition

A number of papers have studied the impact of sociodemographic variables on travel behavior 

patterns. Several studies found significant relationships and variables such as age, gender, household 

composition, and income. Newbold et al. used the General Social Survey dataset in Canada to 

determine the travel pattern differences of older (65+) and younger people.(27) The data are available

for different time periods (1986, 1992, and 1998) and can therefore also control for generational 

differences. The study found significant differences in trip duration and frequency across generations.

Employment level and health status were also significant predictors of trip duration and frequency. 

Gender differences in trip duration and frequency as well as mode choice are significant in many 

studies, which attest women to be more likely to change their behavior toward more sustainable 

travel modes.

(28,29)

Moriarty and Honnery and Best and Lanzendorf found no significant differences

between men and women in total number of trips and distance traveled but found differences

in activity types.(30,31) Whereas men make more work trips, women make more journeys for 

maintenance activities. Researchers consistently find that household composition influences trip 

type, duration, and frequency. Key stages in households include the gain or loss of employment, 

children, and retirement.

(32) Student, unemployed, and part-time employed households with no 

children are more likely to use non-motorized transportation, and high-income and retiree households

are less likely to use non-motorized transportation. Car ownership, also endogenous to some model

systems, is found to be significant in many studies, particularly with high-income groups, with a 

tendency to use cars versus public transit.

(33) Guilano, Guilano and Narayan, and Guilano and 

Dargay studied differences in travel behavior between different sociodemographic groups in the 

United Kingdom and United States.

(34–36) According to the studies, Americans make 4.4 trips per 

day with a length of 43 mi (70 km) compared to 3 trips per day and 16 mi (26 km) in the United 

Kingdom. In both countries, travelers over age 65 traveled roughly half the distance of younger 

participants. The difference between the countries was explained by the lower income and 

significantly higher transport costs in the United Kingdom.

Bomberg and Kockelman surveyed more than 500 Austin, TX, commuters to gather information on

driving behavior during and after an abrupt increase in fuel prices.(37) For most of summer 2005, 

price increases were comparable to previous years; however, between August and September, prices 

increased 36 percent, from $2.16/gal ($0.57/L) to $2.93/gal ($0.77/L). Ordered-probit models to 

classify the behavior change suggest that travelers are most likely to respond by reducing overall 

driving through increased use of other modes or trip chaining. A traveler’s built environment 

characteristics were more influential in behavior change than even income, education, and average 

driving. Some drivers adapted their driving style, suggesting the use of a series of strategies to cope 

with system changes. Respondents were surveyed again in 2006 to gather information about response

to transportation policy measures. Though there was substantial support for alternative modes and

reduced fuel dependency, respondents’ willingness to pay for driving increased ($1.45/gal ($0.38/L)) 

as distance from the central business district increased by one standard deviation from the mean 

(3.74 mi (6 km)). 

In these studies, some urban form variables were evaluated in addition to traveler characteristics. 

Residents of less dense urban areas tend to travel farther. Thus, density influences the price of 
11

travel and therefore travel behavior.

(38) In the United States, urban form is thought to reinforce 

car use and dependency.

(35)

Effect of Travel Demand Management Measures and Parking Pricing on Mode Choice

TCRP Report 95 indicates that eliminating minimum parking space requirements and charging 

market rates for residential parking spaces could reduce vehicle ownership per household enough to

reduce household VMT by 30 percent.(39)

In the same report, charging employees for parking at work 

was linked to a 10–30 percent decrease in SOV mode share, depending on the quality of transit 

alternatives. In Portland, OR, establishing maximum parking ratios and a “parking lid” appeared 

to reduce the downtown parking ratio by half, from roughly 3.4 long-term spaces per 1,000 ft2

(93 m

2

) of commercial space in 1973 to 1.5 per 1,000 ft2 (93 m2) in 1990.

(40)

These parking policies

along with some travel demand management measures and transit enhancements are credited with

increasing Portland’s “downtown transit share from 20–25 percent in the early 1970s to a downtown

commuter transit share of 30–35 percent in the 1980s and 1990s.”

(40)

Many urban design variables 

influence mode share. For example, cities with few parking spaces per employee tend to have 

higher transit mode share, as expected, since limits on parking are implicitly reflected in the 

shadow price associated with parking.

(40)

Using the 6-week Mobidrive study, Schlich and Axhausen explored repetitious travel behavior.(41)

As humans rarely evaluate all their options anew at each opportunity and constraints are relatively

similar from day to day, habits are formed but mediated by each day’s changing needs. Schlich and

Axhausen found that behavior is more variable on weekend days than working days.(41)Variability

declines over time, and for each individual in the study, variability was sharply reduced and constant 

after 2 weeks (i.e., the respondent looked similar over 3 weeks and over 5 weeks). They 

recommended that participants be observed over 2 weeks.

Learning, Experience, and Inertia

Inertia, a traveler’s propensity to continue making the same choices based on past experience, is not

yet well understood. Recently, Cherchi and Manca demonstrated that the significance of inertial 

effect varies substantially with model specification, and this effect is not stable during a statedpreference (SP) experiment.(42)

Depending on a participant’s past experience and exposure to options, 

the inertial effect also varies, pointing to a need for well-designed and controlled experiments. 

Using a regret-based model employing Bayesian perception updating, Chorus et al. determined a 

perceived value of acquiring travel time information as the difference between expected regret 

induced by a choice before and after acquiring information.(43) Simulations revealed that this 

value, even for drivers who consider transit as an alternative to driving, is influenced by three 

factors: information irrelevance, information unreliability, and preference for driving. These 

same factors also limit the effect of received information on mode choice when the information 

is highly favorable toward transit. The authors suggested only transit information that is freely 

provided and easily accessible has the potential to be used by drivers. This information should 

also be reliable and include aspects of comfort, dynamic conditions, convenience, and perhaps 

even environmental friendliness. Given the difficulty in meeting these conditions of low-cost, 

high-quality information, Chorus et al. suggested it may be more efficient to demonstrate the 

car’s limited attractiveness in certain conditions, such as inclement weather or road accidents.
12

LIFESTYLE- AND MOBILITY-BASED BEHAVIOR CHANGES

This section primarily examines urban form variables and the self-selection phenomenon to 

understand travelers’ lifestyle choices. The influence of added network capacity on travel 

behavior is briefly discussed as it relates to traveler characteristics.

The effects of price and traveler characteristics on utility are well understood, but attitudes about 

mobility and lifestyle and how these attitudes manifest in behavior are still not well understood. 

There are studies that examine the influence of psychosocial attributes besides income on car 

ownership.(44)

Hiscock et al. in Scotland and Cullinane in Hong Kong found psychosocial benefits 

in car use, especially for young males.(45,46)Car owners in these studies felt car use improves prestige, 

protection, autonomy, and self-image. 

For decades, the supply-oriented approach to transportation planning revealed that network 

equilibrium will always result in increasing travel in response to increased capacity such that adding 

additional capacity only alleviates congestion in the short term. Furthermore, adding freeway 

capacity is thought to induce additional travel, so Fujii and Kitamura explored the relationship 

between individuals’ activities and the travel environment to determine whether this is the case for 

commuters between the times they leave work and the times they go to sleep.(47)The authors used

structural equations to analyze the impact of hypothetical freeway lanes in Japan’s Osaka-Kobe 

metropolitan area on residents’ time use and travel. The model examined the number of trips during

this period, the total out-of-home activity and travel durations, the number of home-based trip chains,

and the total amount of time spent at home after arriving for the first time until going to sleep. Their

model of travel preferences suggested that older, married individuals tend to have a lower preference

toward both in-home and out-of-home activities, meaning they have lower preferences toward all 

activity types. Individuals with higher incomes have large preference indicators for both in-home 

and out-of-home activities but more so for out-of-home activities. Because time-use and travel 

variables were treated as endogenous in this study, the impacts of supply changes cannot be 

thoroughly addressed. However, the results suggest that additional freeway lanes induce very little

traffic, indicated by only slight increases in number and duration of out-of-home activities. Much 

of the time savings from added capacity is allocated to in-home activities.

Effect of Transit-Oriented Development/Density on Behavior Patterns and 

Long-Term Choices

Much of the available research on travel behavior and land use interactions is aggregate analysis. 

The focus on the relationship between urban form and aggregated travel patterns provides little 

insight into the underlying factors and mechanisms by which urban form influences individual 

choices.

(48) Disaggregate analysis using analysis of variance or regression to study householdand individual-level behaviors suggests that behavior differences are greater among neighborhoods 

than among individuals within neighborhoods and that attitudes play a very important role in 

decisionmaking. Handy noted there is a need to understand how urban form shapes choice sets, 

since discrete choice theory is only able to illustrate how factors influence choices within a given 

choice set.(48)

Holtzclaw et al. attempted to determine which factors most influence home location selection 

and associated transit use.(49) Using odometer readings from emissions systems inspections in 
13

San Francisco, CA, Chicago, IL, and Los Angeles, CA, the authors predicted a household’s VMT

as a function of home-zone density, proximity to jobs, transit service and access to jobs by transit, 

availability of local shopping, and pedestrian and bicycle friendliness (i.e., the attractiveness of 

these options compared to driving).

(49) The elasticities for vehicle ownership with respect to 

density for Chicago, Los Angeles, and San Francisco were -0.33, -0.32, and -0.35, respectively. 

Elasticities for VMT (per capita) with respect to density were -0.35, -0.4, and -0.43. Since residents 

of these cities have above-average access to transit and the model did not control for costs of 

parking, income, and other relevant variables, applying this model across more cities may not 

yield such results. For example, the model does not control for attitudes toward driving and 

public transit, differences in living or vehicle-ownership costs, or the cost and quality of transit. 

These variables differ significantly in most major U.S. cities, and attitudes are typically a very 

strong influence on travel patterns. However, the magnitudes are surprisingly similar for three 

urban areas that differ significantly in terrain and climate. Density often acts as a strong proxy 

for other urban characteristics. 

Equally important to the understanding of how these factors reduce VMT is an understanding of 

what factors individuals most prefer in neo-traditional developments. In Lund’s survey, California 

residents were asked to identify their top three reasons for choosing to live in a transit-oriented 

development. Only 33.9 percent cited transit accessibility as a top reason.

(50)

More often, residents 

preferred type or quality of housing (60.5 percent), cost of housing (54 percent), or quality of

neighborhood (51.7 percent). Lund also found that residents who listed transit as one of their top 

three reasons were 13–40 times more likely to use transit than those who did not, suggesting

significant effects of self-selection in such developments. This endogeneity is the topic of the 

following section.

Residential Self-Selection and Vehicle Ownership

Researchers have sought to disentangle the impact of travel preferences and self-selection in home

location choice and how this choice ultimately impacts differences in observed travel patterns 

across distinct neighborhood designs. Cao et al. suggested that attitudes and sociodemographics

are confounding influences in such studies.(51) While definitive conclusions have not emerged, 

general neighborhood design distinctions (e.g., walk-oriented versus auto-oriented, existence of 

bicycle lanes, distance to work and non-work locations) appear responsible for at least half of 

the observed VMT differences. (See references 51–53 for discussions of literature and results.)

Surveys conducted in Atlanta, GA, by Frank et al. revealed that despite driving preferences, 

residents living in a walkable neighborhood tended to drive far less than those living in auto-oriented

neighborhoods.(54)

The least walkable neighborhoods generated roughly 45.5 mi (73.3 km) of travel 

per worker per day while the most walkable generated only 28.3 mi (45.6 km). Furthermore, those

who preferred an auto-oriented neighborhood but happened to live in a walkable neighborhood 

tended to drive significantly less (25.7 mi (41.4 km) per day per worker) than their counterparts 

in auto-oriented neighborhoods (42 mi (67.6 km)), despite their stated preference. Of those who 

preferred walkable neighborhoods, the VMT per day per worker values averaged 25.8 and 36.6 mi

(41.5 and 58.9 km) for residents of walkable versus auto-oriented neighborhoods, respectively. 

Thus, while someone may prefer to live in a different neighborhood, it appears that he/she will 

conform to the travel opportunities of the home neighborhood. Households residing in suburban 

settings (versus more traditional neighborhoods) tend to be older and have more members. As 
14

expected based on VMT patterns, they also own more vehicles per household member (see, for 

example, reference 55). The neighborhoods in the Frank et al. study had similar densities but 

differed in household size and income.(54)

More recently, Aditjandra et al. applied dynamic (quasi-longitudinal) structural equation models 

to understand residential self-selection in the United Kingdom.(56) This method was demonstrated 

in a U.S. context by Cao et al.

(57)

In the United Kingdom study, 219 participants who had moved to 

their current residence in the last 8 years were asked how they drive now compared to before they

moved on a 5-point scale from “a lot less” to “a lot more.” Results suggest that sociodemographic

characteristics are the main influence on changes in car ownership, but changes in neighborhood 

characteristics—in particular, safety factors and shopping accessibility—had an important influence.

These findings corroborate Cao et al.’s suggestion that, controlling for residential self-selection, 

neighborhood design impacts on travel behavior “may be similar in different geographical settings

despite different planning contexts.”

(56,57) In the United States, car ownership is associated with 

yard size and availability of off-street parking, whereas in the United Kingdom, shopping/facility 

accessibility and safety of residential neighborhoods most influences vehicle ownership. Again, 

such variables can often proxy for other characteristics; for example, yard size could indicate 

home lot size or that the residence is a single-family dwelling.

These proxy issues point to the need to better understand human interactions and the mechanisms 

that drive behavior. After all, if a family moves, friends may still live in the old neighborhood and 

exhibit the former travel behavior, and as many studies have shown, geography is one of the best 

indicators of frequency and duration of social contact.

BEHAVIORAL MECHANISMS

Besides all the influencing factors and characteristics that explain travel behavior changes, it is 

important to understand the underlying process of the perception and manifestation of these 

characteristics, which then lead to a behavior adjustment. That is, how do patterns become lifestyle 

choices? Even though there are day-to-day travel variations, travel patterns repeat themselves, which

suggests that parts of travel behavior are habitual and influenced by inertial effects.(58)Furthermore,

the effect of information depends on whether travelers comply with the prescribed information. 

Inertia, information compliance, travel experience, and learning determine system outcomes that 

feed back into supply and demand models. 

Behavior adjustment implies that behavior is an outcome of experience or new information about 

current conditions. This can be seen as a learning process that leads to an adjustment of behavior. 

Mahmassani and Chang studied adjustment- and experience-based models of perceived travel time

for departure time choice.(59)Under the myopic adjustment rule, the perceived travel time is only a 

function of the latest day’s outcome. In laboratory experiments conducted to study the effectiveness

of different information strategies on user responses to information, Srinivasan and Mahmassani

found that route switching model specifications, which predict whether a user will switch paths in

a given time interval, consistently outperformed models that view the process as a new choice at 

every opportunity.(60)These mechanisms are neither mutually exclusive nor collectively exhaustive,

meaning they can operate simultaneously and in conjunction with other mechanisms. The authors

designed an experiment whereby virtual commuters were given trip times on three facilities (at 

decision locations), real-time information about congestion on the facilities, a message alerting the
15

driver when he or she was stuck in a queue, and post-trip feedback consisting of departure time, 

arrival time, and trip time on the chosen path. Their empirical findings suggest that an individual’s

negative experience with advanced traveler information systems (ATIS) has mixed effects on 

inertia, but congestion and information quality tend to reduce inertia. Drivers who experience 

lower switching costs and increased trip time savings tend to comply with information. In the 

sequential treatment, past negative experience relative to preferred arrival time seemed to increase 

likelihood of compliance. Inaccurate information decreased drivers’ compliance propensity. 

Bayarma et al. examined multiday travel behavior as a stochastic process using 6-week travel diary 

data, exploring how travel patterns vary and persist among heterogeneous individuals.(61)The authors 

classified weekday travel patterns into five representative patterns: public transport commuting;

extensive car use involving three or four visits to a location; three to four shopping, leisure, and 

social trips; high fraction of trips that serve to transport another person; and mostly work visits and

time spent on work-related activities. The authors found that transitions from a pattern to itself are

frequent, especially for non-workers, but transitions from pattern to pattern vary substantially across 

individuals. Individuals with a driver’s license tended to have a higher level of day-to-day variability 

in their travel patterns. Residential location type also influenced variability in daily travel, with 

individuals living in a central area regularly pursuing more shopping and leisure activities. Gender, 

marital status, and number of household vehicles were insignificant in this study—age, household 

type, and employment status explained much of the variation. 

A seminal work on attitude-behavior theory addressed the interrelationships between attitudes 

and behavior from multiple modeling perspectives, including multiattribute, hierarchical, market 

segmentation, and, to a lesser extent, structural equation models.(62) Simple models provided

empirical support for behavioral feedback mechanisms, and attitudes and behavior were found to 

simultaneously influence one another. The concept of simultaneous influence has been explored 

in greater depth since the study, and market segmentation and structural equation models are still 

used to explore psychosocial influences in travel behavior. Beyond attitude, perception and intention 

have a substantial influence on behavior. While attitude and perception have been explored in great 

depth, less attention had been paid to traveler intention until recently. Bamberg found that forming 

an implementation intention (when, where, and how to perform an action) increases the probability 

that a goal intention is manifested in behavior.(63) In a study of 90 university students, forming an 

intent to ride a new bus route was the best predictor of whether a student rode the new bus route, 

even more so than current bus- and auto-use habits. While habit exerted a strong negative effect

on whether one would test the route for the control group, habit did not strongly influence the 

experimental group. Thus, Bamberg points out that influencing behavior involves not only 

influencing the decisionmaking process but also the formation of implementation intention.(63)

Household Interactions

The dominant paradigm in travel behavior is the individual satisfying needs while maximizing the

utility derived from the activities undertaken. That this undersocialized understanding severely 

limits the scope of its work has been understood since the mid 1970s, but only in recent years 

have researchers developed a set of methods and models that can replace the previous generation 

in practical application.

(64) Even though the social context of traveling is underresearched, the 

importance of joint activity participation is evident and has been studied. Kostyniuk and Kitamura

analyzed time-use data and found that joint activities tend to have a longer duration than other 
16

similar non-work activities.(65) Furthermore, people participating in joint activities travel farther 

to perform an activity. Household level and social network size influence traveler choices at all 

levels, from departure time and route to residential and employment location selection.

Many researchers have studied the effect of household attributes on joint activity travel. For 

example, Jones et al. and Kostyniuk and Kitamura found that adults are strongly affected by the 

presence of children.(64,65) Couples with children perform most joint activities at home, whereas 

couples without children are more likely to perform joint activities outside the home. Employment

status influences the starting point of joint activities; couples in which both are employed tend to 

choose a starting location outside the home. The same research also found that the availability of 

a car positively influences individual time-use patterns of couples. Fujii et al. found that people 

rated time spent in non-work joint activities higher than non-work activities spent alone.(66) Time 

spent in joint activities was rated more “satisfying,” and people chose to allocate time to joint 

rather than independent activities if possible. Freedman and Kern researched the implications 

of two-worker household status on location choices and concluded that wives’ commute burdens 

influence home and workplace location decisions.(67)

In a similar study of time use via detailed in-person interviews with 30 dual-career households in the

United Kingdom, Green found that residential site selection depended more on the working male’s 

job location, even in households that had recently moved.(68)The interview results and census data

suggested male worker commute times in the United Kingdom in 1995 were declining with respect

to 1980 commute times, while those of female workers were increasing in dual-career households.

Men’s commute times were still longer, with roughly two-thirds of males commuting more 

than 30 min to work and about half of females commuting more than 30 min. Green expected the

long-run convergence of male and female commute patterns in the United Kingdom, and American 

Time Use Survey data suggest this happened in the United States as of 2007 or earlier.(68,69)

Srinivasan and Bhat found that wives’ in-home maintenance durations were the most susceptible to

change based on household attributes and husbands’ activity choices.(70) Out-of-home work duration 

and commute time negatively impacted husbands’ in-home maintenance time, while the number 

and age of children had no effect. To accommodate this, wives’ in-home maintenance time increased

with their husbands’ out-of-home work durations, the number of children under age 5, and the 

availability of a personal vehicle. Females’ commute times were not found to affect their in-home

maintenance times.

Lee et al. used simultaneous Tobit models for Tucson, AZ, data to model household time 

expenditures.(71) Their results suggest that the number and work status of household heads are 

primary determinants of trip chaining and time allocation. Interestingly, income and vehicle 

ownership levels were not found to be strong predictors of chaining behavior. More recently, 

Lee et al. used 2001–2002 Atlanta, GA, survey data and land use files. As expected, they found 

that people with children over age 6 spend less time traveling and those with very young children 

(under age 5) spend less time in out-of-home subsistence and discretionary activities.(71)

Hence, time and task allocation at the household level were incorporated in the models. (See 

references 72–75.) However, individuals are part of social networks, and behavior is influenced 

by others’ attitudes and behavior. Thus, joint activities do not only involve household members 

but may also include the social network. Axhausen noted that in addition to the generalized costs 
17

of travel and the hedonic utility of a location (as modulated by the sociodemographics of an 

individual and perhaps his/her values, attitudes, and lifestyle), the geography of the social 

network of the person should be included in models.

(76)

Social Networks

More studies, including surveys and data collection efforts, have recently focused on the influence 

of social networks on travel behavior. (See references 77–81.) A number of new models and 

simulations have also shown this influence.(82,83)These studies mostly explored the cross-sectional 

relationships between characteristics of social networks and physical and virtual travel. Van den 

Berg et al. used a social interaction diary to study the factors influencing the planning of social 

activities.(84) The researchers found that social activities scheduled later in the day are less likely 

to be routine. In contrast, social activities of longer duration and taking place on weekends are more 

likely to be routine or preplanned. Harvey and Taylor studied the influence of work location on joint 

activities with time-use data.(85)They argued that people who work at home spend more time alone

and therefore show a tendency to travel more to fulfill their needs for social interaction. Carrasco 

and Miller described joint activity participation with egocentric social structure effects (degree of 

a person), the use of communication technology, and sociodemographic variables.(77) They found 

that people with a high egocentric social network degree are more likely to perform joint activities.

The availability of communication technology such as telephones and the Internet reduces the cost

of coordination and influences participation in joint activities. Further, information dissemination 

within a social network could change attitudes and perceptions, leading to changes in travel 

behavior.

(86,87) To date, information dissemination in a travel behavior context has not been 

examined further than numerical experiments. The complexity and lack of data have hindered 

the incorporation of social network concepts into full transportation demand models. This 

issue is discussed further in later sections of this report.

Market researchers and behavioral scientists have examined how new behaviors are adopted and 

to what extent adoption may be a function of a personal identity and social norms, particularly 

social acceptance. A great body of work is concerned with adoption of new technologies and 

purchasing behaviors, but little research has been done regarding adoption of modes and 

transportation behaviors of one’s close or extended social networks.

19

TRAVEL BEHAVIOR MODELS REVIEW

ACTIVITY-BASED MODELS

The advantages of activity-based models are diverse but can be organized in to the following

four categories, which are later discussed in more detail:

• They can identify the influence of time, destination, and mode on trip attributes.

• They capture long-term behavior, overcoming the limitations of tour-based models by 

including activity patterns outside the daily schedule in addition to time dependency, 

destination, and mode.

• They capture certain characteristics of individual-based decisions beyond aggregate 

traffic analysis zones.

• They capture short-term decision shifts that may have substantial impacts at the network 

level. Linkage of interpersonal decisions that are crucial to policies such HOV lanes can 

be taken into account.

These are also the main reasons to incorporate an activity-based framework rather than a trip- or 

tour-based framework.

Within activity-based travel demand models, there are two primary modeling paradigms: utilitybased econometric models and rule-based computational process models. These models are not 

exclusive but have different philosophical approaches and are therefore fundamental in how the 

travel generation process is understood. Combinations of the two models can be found within 

agent-based simulation models, which are discussed later in this report.

Utility-Based Econometric Models

Utility-based econometric models have their roots in economic consumer choice theory, which says

that individuals maximize their utility from the choices they make. These models consist of a number

of different choice-based models for the individual’s activity-travel decisions. These models can be

enriched by other utility-based models, such as hazard models for time durations. The set of economic 

equations builds the structure to model the relationships among the traveler characteristics, the 

network characteristics that allow the individual to travel, and the environment characteristics

that describe the place to perform activities and further restrictions on the traveler’s behavior.

The following are existing utility-based econometric activity-based model systems:

• Greater Portland METRO.

(88)

• San Francisco County Transportation Authority.(89)

• New York Metropolitan Transit Council.(90)

• Mid-Ohio Regional Planning Commission (Columbus, OH).(91)
20

• Sacramento Area Council of Governments.

(92)

• Atlanta Regional Commission.(93)

• Comprehensive Econometric Micro-Simulator for Daily Activity-Travel Patterns.

(94)

• Florida Activity Mobility Simulator.

(95)

All of the listed models can be categorized as full individual day pattern models or linked full 

individual day pattern models. The full individual day pattern models follow the concept of an 

overarching daily activity-travel pattern proposed by Bowman and Ben-Akiva.

(96)

These models are 

based on an underlying system of multinomial logit or nested logit models in a particular hierarchy. 

The linked full individual day pattern models enhance the models previously described by allowing 

intrahousehold interactions in activity-travel engagement models. The Columbus, OH, and 

Atlanta, GA, models are examples of such models.

Types and Advances

To meet expectations and improve analyses, one of the natural responses from transportation 

modelers is to develop more sophisticated modeling forms, ranging from simple logit and probit 

to nested and mixed logit to Bayesian procedures and so on. The travel behavior model evolved 

from simple regressions based on aggregate, revealed preference (RP) data to more sophisticated 

mathematical functions based on disaggregate, SP data.

(97)

For example, the logit family of models 

progressed from the binary logit model to the multinomial logit model or the conditional logit model

and then to the nested logit model and the mixed logit model. As early as 1972, the binary logit 

model was utilized in intercity travel mode choice. The multinomial logit model was primarily used

to model multiple choices. Ben-Akiva derived the nested logit model that is designed to capture 

correlations among alternatives.(98) At the time of this report, mixed logit is considered the most 

promising discrete choice model that is intuitive, practical, and powerful. It combines the flexibility 

of probit with the tractability of logit.

At the same time, a large number of transportation professionals have devoted effort to identifying

and evaluating major utility factors besides time and cost to improve the predictability of utility 

functions. Hensher used an early example to incorporate comfort and convenience in a travel mode 

choice model.(99) Algers et al. included comfort and convenience in a study on the value of travel 

time.(100)

Later, Liu et al. proposed a conceptual framework that includes travel time, monetary cost, 

comfort/convenience, and safety/security in the travel choice models.(101)In a recent choice model, 

Ben-Akiva et al. integrated latent variables to model attitudes and perceptions and their influence 

on choices.(102)

The underlying concept to understand behavior changes from operational interventions has to 

account for the problem that behavior switching is not the same as observation of a certain behavior. 

Behavior switching is less rational, as travelers tend to stick with what they are used to, which can

be described as inertia. The decision rule is no longer based only on maximizing the utility but 

includes a switch if the utility of switching exceeds a threshold. The generalized indifference 

band framework is shown in figure 2.

Pr(X =1) ⇐⇒ Pr(U ≥ 0)

Figure 2. Equation. Generalized Indifference Band Framework.
21 

The random utility formulation has to account for time and location at different days and can be 

formulated as shown in figure 3. 

Uijt = Vijt + εijt

Figure 3. Equation. Random Utility Formulation. 

Where: 

Vijt = f (Zi, Nijt, NNijt, Vijt). 

Zi = Traveler characteristics. 

Nijt = Time-dependent network attributes by decisionmaker i at decision location j at day t. 

NNijt = Time-dependent non-network characteristics by decisionmaker i at decision location j at day t. 

Vijt = Time-dependent vehicle characteristics by decisionmaker i at decision location j at day t. 

εijt = Error terms correlated over different times of day, locations, and days. 

To incorporate interpersonal interactions, a utility maximization approach can be included. Each 

individual’s utility is calculated jointly and singly, and a joint decision is made if the difference 

of the best alternatives exceeds a certain threshold, as shown in figure 4 and figure 5. 

ΔUsi = ෍Uijtsingle − ෍Uijtjoint

Figure 4. Equation. Joint Versus Single Utility Threshold. 

ΔUT = ෍(αiUsi + ... + αkUsk)

Figure 5. Equation. Weighted Sum of Joint Versus Single Utility Threshold. 

Where: 

α = Each individual’s weight of influence. 

Rule-Based Computational Process Models 

Rule-based computational process models are developed on the premise that individuals do not 

always act rationally and so do not maximize their utility. Instead, individuals rely on a process 

that contains complex if-then rules to solve a task, similar to a production system model. These 

models have problems describing the statistical significance of the factors that affect the rules 

and are therefore not always best for understanding behavior changes based on experiments or 

for predicting future changes. The following are existing rule-based computational process 

activity-based model systems: 

 CARLA: Combinatorial Algorithm for Rescheduling Lists of Activities.(103)

 STARCHILD: Simulation of Travel/Activity Responses to Complex Household 

Interactive Logistic Decisions.(104)
22

• SCHEDULER.

(105)

• AMOS: Activity-Mobility Simulator.

(106)

• SMASH: Simulation Model of Activity Scheduling Heuristics.

(107)

• ALBATROSS: A Learning-Based Transportation Oriented Simulation System.

(108)

• TASHA: Travel Activity Scheduler for Household Agents.

(109)

Agent-Based Modeling Systems

The Transportation Analysis and Simulation System (TRANSIMS) and the Multi-Agent Transport

Simulation Toolkit (MATSim) are agent-based activity-based modeling systems that were originally

designed to account for the full disaggregate representation of individual travel behavior.(110,111)

They were mainly developed to capture characteristics of individual-based decisions beyond

aggregate traffic analysis zones and short-term decision shifts that may have substantial impact at 

the network level in conjunction with dynamic traffic assignment. Thus, the feedback is based on 

aggregated system information, not on specific agent information. If one is not interested in the Nash 

equilibrium, traditional dynamic and non-dynamic traffic assignment approaches fail because there is

no access to human behavior.

(112)

Since these agent-based systems do not find a clear equilibrium,

it is unclear what solution they produce. Further, by decoupling the demand side (activity-based 

models) from the supply side (either assignment or simulation models), the activity models 

typically compute probabilities for a large number of alternatives, which demands an explicit 

choice set. Accounting for such alternative sets in real-size networks would result in very long 

computation times.

(113)

MODEL INTEGRATION

As previously discussed, the development of advanced travel behavior and demand models and 

the development of transport supply models have been relatively independent of one another. Thus,

the demand and supply models are each formulated to use forecast outputs from the other model 

without feedback. As a result, the level of service input to demand models is not necessarily the same

as that output from the supply models nor is the demand input to supply models necessarily the 

same as that output from the demand models. Thus, it is important to integrate demand and supply 

models to ensure consistency between supply and demand. Efforts to integrate demand and supply 

models have been made in recent years. Lam and Huang presented a mathematical formulation for

dynamic traffic assignment for modeling simultaneous location, route, and departure time choices.(114)

Their model can be used as a simplified travel demand analysis tool but cannot capture travel

behavior complexity. Lin et al. proposed an integration of an activity-based model simulator with a 

dynamic traffic assignment model, where feedback convergence is measured by aggregated travel 

time and number of trips.(115)The authors showed that the initial differences are substantial and can 

be reduced dramatically on an aggregated level. Lu and Mahmassani presented a “joint route and 

departure time network equilibrium assignment model explicitly considering heterogeneous users 

with different preferred arrival times at destinations, values of time, and values of early and late 

schedule delays.”

(116)

Their “multicriterion simultaneous route and departure time user equilibrium”

determined both changes in route choice in response to dynamic pricing as well as temporal shifts 

toward less congested periods. Application of this model in a large network setting remains limited 
23

by computational capabilities, but the model can realistically be applied to alleviate congestion 

by finding optimal dynamic pricing schemes by location, pricing periods, and toll charges.

On a completely disaggregate level, some agent-based simulations also incorporate demand and 

supply models. Esser and Nagel and Rieser et al. developed a multiagent microsimulation module

that integrates activity generation, route assignment, and network loading.(117,118) Generated daily 

activity-based plans are executed by agents and assigned to the route. The traffic simulation is then

used to evaluate those activity plans. Changes in activity start times, route choices, etc. are 

randomly adjusted and high-scoring activity plans are executed. 

Although the need for integration of transportation demand and supply has been accepted for 

years, much of the research is still in the conceptual stage.

25

TRAVEL BEHAVIOR DATA REVIEW

STANDARD SOURCES

Most travel behavior studies use surveys and travel diaries as data sources. There are only a few 

laboratory and field experiments that have been used for collecting data to capture travel behavior. 

The most widely used resources are national travel surveys that many countries collect. These 

surveys typically collect individual information (socioeconomic, demographic), household 

information (size, structure, relationships), vehicle information (age, make, model), and a 

diary of journeys on a given day (start and end locations, start and end times, mode of travel, 

accompaniment, purpose of travel). Recent and continuously collected national travel 

surveys are summarized in table 1. Armoguum et al. gave an overview of the different 

national travel surveys.(119)

Table 1. Recent and Continuous National Travel Surveys.

Country Source Years

United States Federal Highway Administration 2009, 2001, 1995

Germany German Mobility Panel 1994–2011

Denmark Danish Transport Research Institute 2006

Netherlands Institute for Road Safety Research 1978–2011

United Kingdom United Kingdom Department for Transport 2009

Switzerland Federal Department of Environment, Transport, Energy 

and Communications

2010, 2005, 2000

Sweden Sika Institute 2006

A number of papers accessed data from secondary sources such as the American Housing Survey 

2001 or relied on specific surveys designed for the research. For example, Srinivasan and Rogers 

used a random stratified sample of 500 households in two suburbs of Chennai, India; Ampt et al.

interviewed 102 participants in their survey of household travel behavior; and Rose and Ampt 

included only 46 participants in their study of car use reduction strategies in Sydney and Adelaide, 

Australia.

(120–122) Thus, the number of participants in research projects varies considerably from 

less than 100 to many thousands in the case of national travel surveys. Methods of data collection 

also vary considerably, including face-to-face structured or semi-structured interviews, postal 

questionnaires, telephone surveys, and, more recently, online questionnaires.

TRAVEL DIARIES

Most studies of travel behavior use travel diaries, which include data from trips and the activity 

behavior that is collected with the trip. The most common practice with travel diaries is to collect 

data from a 1- or 2-day time span, which has a relatively low response burden and gives full 

information about trip frequencies, mode choice, and other decisions for aggregated models. 

Boarnet and Crane, Bowman and Ben-Akiva, Giuliano, Kunert and Follmer, and Newbold et al.

all used 1-day travel diaries. (See references 38, 96, 34, 123, and 27.) A number of other projects 

used 2-day travel diaries.

(23,124,125) However, to observe behavior changes and habitual travel and 

record data about how to influence and change travel behavior, a longer reporting period would be
26

useful. Only a small number of research projects used 7-day or longer diaries, including Garvill et al.,

Kenyon, and Schlich and Axhausen.(126,127,41) In Schlich and Axhausen’s study of travel behavior in 

Karlsruhe and Halle (Saale), Germany, participants were required to keep a travel diary for a period 

of 6 weeks, recording all travel movements during that time. A total of 52,273 trips were recorded

by the 361 participants.(41) Garvill et al. used the German National Travel Survey, a panel survey 

where participants are required to complete a travel diary several times during different periods.(126)

Kenyon required 100 participants to complete a 7-day travel diary three times at 6-month intervals

in March 2004, October 2004, and March 2005.(127)

Not all surveys state the day of the week and season for the trips reported. Often, the day is only 

differentiated between weekdays and weekends/holidays. It is surprising that the season and day 

of the week are not discussed, as seasonality and associated weather has an influence on travel 

patterns, and travel patterns vary from day to day and week to week.

Larson and Poist provide an interesting overview of all papers using a postal questionnaire 

included in Transportation Journal between 1992 and 2003.(128) The authors report a total of 

106,300 mailed questionnaires and that response rates have declined significantly since 1992.

STATED PREFERENCE AND REVEALED PREFERENCE

To analyze likely behavior changes in response to changes in the transport system and other 

influence factors, RP data are often unavailable and SP data have to be used. As a result, significant

literature has been developed around survey methods for estimating individuals’ behavior adaption

in the absence of revealed system variation. These methods are widely used for developing optimal 

pricing strategies, forecasting responses to price changes, and modeling demand functions.

Although longer time studies might have information about system changes, there are often 

limitations to RP data. For example, observation of choices might not occur and changes take time 

for adoption. These limitations could be overcome with real-life controlled experiments; however, 

opportunities for such experiments have been limited. The Federal Highway Administration

provides some opportunities to design and collect data from such field experiments through 

the integrated environment at the Saxton Transportation Operations Laboratory, which has a 

data resources test bed, a concept and analysis test bed, and a cooperative vehicle-highway test

bed. If such experimental environments are not available, SP surveys provide an approximation 

by asking questions about hypothetical situations. The design and configuration of such questions

is not trivial and has been the subject of research in recent years. Bliemer and Rose proposed an 

approach to generate an efficient experimental design that minimizes standard errors in estimating 

the parameters from the utility function that underlies travel behavior decisions.(129)

Because SP data may not include the history of individuals, which is needed to assess the effect of

habits, models estimated with SP data often overestimate the impact of changes in the system on 

behavior adaption. Questions included in SP surveys such as car use and trip frequency can give 

hints about the history so that models can capture inertia. Mixed RP/SP data include more likely 

inertia indicators, as they are constructed based on previously chosen alternatives and real situations

are used to construct the hypothetical SP survey response.

(130) Since such data is vulnerable to 

serial correlation, Cantillo et al. proposed discrete choice models with both inertia and serial 

correlation with mixed RP/SP data. They found that inertia and serial correlation in mixed RP/SP 

data are significant.(131)
27

EMERGING POTENTIAL IN DATA COLLECTION

In 1997, Kitamura and others pointed to the need for more extensive data and improved 

methodologies for understanding travel behavior.(132) The case for collection of such data has 

strengthened in the last 15 years as others have called for more comprehensive transportation 

modeling and planning contexts.(58) Beyond trips and travel networks, analysts need data to 

understand “why, with whom, where, and when activities are engaged in and how activity 

engagement is related to the spatial and institutional organization of an urban area.”

(132)

If travel is

assumed to be a derived demand, then sociodemographics and attitudes are thought to be the primary 

inducements for observed travel. When travel for its own sake is evaluated, attitudes and perceptions 

become even more important.

(133)

From this perspective, travel control measures and travel demand

measures affect urban quality of life more than facility expansion.(133)Thus, any effort to understand

the impact of demand-side interactions must examine human time use. This requires improved data

collection and methodologies that are able to evaluate both induced and suppressed travel as well as 

the implications of this travel on perceived quality of life.

(132)

Time-use data are particularly useful 

for understanding location substitution of activities (e.g., telemobility alternatives) and activity and 

departure timing. Jara-Diaz noted that as the utility of travel depends on activities as a result of 

time and monetary budget assignments, then travel must be understood in the context of human 

activities and the nature and perception of time use.(134) Thus, meaningful models require linking 

data, methods, and knowledge from sociology, psychology, and economics.(134)

Kwan’s review of time-use research, time-geographic research, and studies on human activity-travel

patterns in space-time discusses the integrated nature of time, space, and information technology 

(IT).(135)Transportation research has explored the implications of IT on time use and travel behavior, 

noting that telemobility alternatives may be complementary, substitutive, or synergistic to traditional

travel behaviors.

(136) Furthermore, mobile devices allow for en-route or continuous adjustment of 

travel plans. A significant amount research on the dynamics of route choice behavior is based on 

laboratory-like experiments that repeatedly ask the participants to respond to hypothetical route 

choices. Because of the lack of detailed, disaggregate spatial and temporal data, much research has 

focused on two dimensions at a time (e.g., location and time use, time use and IT, or location and 

IT).

(137,138) Though the availability of such data represented a significant challenge in the past, the 

current challenge lies in the design of postprocessing algorithms to enable analysis of the massive

quantities of data available from smartphones and increased Internet use. Methods for analysis of 

complex space-time data are also needed.(135)

IT presents an opportunity to relax the time-space constraints often imposed in travel behavior 

studies. The ability to mingle work and non-work activities and locations using mobile devices blurs 

the distinctions between home and work and between public and private.(139) Kwan suggested that

these time-space constraints will not disappear altogether, since IT accessibility and quality of service

is often constrained by location.(140) Future research should examine how social and geographical 

contexts shape the impact of IT on specific social groups and, in particular, urban areas.

(135)

Furthermore, analysts should examine interactions among household members within social groups 

and evaluate within-household variations of IT use. IT provides additional data sources such as 

social networking sites that can be used for a variety of detailed destination attributes and trip 

purposes that were previously unavailable.
28

GROWING ROLE OF EXPERIMENTS AND GAMING METHODS

Experimental methods have an increasing role to play in the study of complex activity and travel 

behavior dynamics, especially as information and communication technologies increase the realm

of spatiotemporal opportunities available for individual and household activity engagement. In a 

synthesis of experimental economics approaches to travel behavior, Mahmassani identified the 

following situations for which laboratory experiments may be needed in the study of the relevant 

behavior and system properties:

(141)

• Complex dynamics and collective effects are essential aspects of the system under 

consideration, making joint measurement in the real world considerably complicated 

or costly.

• Situations or policies of interest are not available in the real world (e.g., new technologies) 

or are mutually inconsistent in the same system.

• Control for extraneous factors is desired.

• Understanding of dynamics and learning processes is of concern.

Experiments that entail varying degrees of sophistication in context design, task design, and delivery 

environment and that contain different scales of experimentation in terms of number of participants

and environmental perturbation are common in many disciplines concerned with the study of human

systems. Transportation planning professionals and travel behavior-activity researchers have been 

slow to adopt experimental methods in research or practice (with the exception of stated response

methods and full-scale operational tests). However, from modest beginnings in the early 1980s, 

there appears to be growing interest in experimental methods for the study of human behavior in 

transportation decision situations. The following reasons can be surmised for this phenomenon:

(141)

• Growing interest in experimental economics as an approach for the study of economic 

systems.

• Related development in complexity science and its application to human, economic, and 

sociotechnical systems.

• Advancement in computing capabilities and networked environments, especially the 

Web, and interest in large-scale collective phenomena in networks.

• Continued development of travel behavior as a focus of interdisciplinary research, with 

entry of professionals from varying disciplinary backgrounds.

• Increased sophistication in methods, theories, and intellectual constructs in travel and 

activity behavior research.

• Significance of policy questions and concerns that require better understanding of 

behavioral dynamics and multiagent interactions (e.g., environmental sustainability, 

vehicle use, and congestion mitigation).

• Technological advances in information and communication technologies that enable 

improved simulation/gaming environments, delivery platforms, and multiplayer interactions.
29

In the mid to late 1980s, Mahmassani and Herman conducted a series of three experiments involving 

actual commuters in a simulated congested traffic corridor.

(11)

Those experiments were conducted

before the widespread availability of personal computers and the Internet and entailed overcoming

significant logistical challenges. The participants were all actual daily commuters who responded

to traffic conditions with their selection of a particular time to depart or route to use in a commuting 

corridor. The experiments provided a basis for articulating a theory of departure time and route 

switching decision mechanisms in repeated decision situations such as work commuting. The 

experiment results were indirectly validated with 2-week diary surveys of commuters in Austin and

Dallas, TX.

(142,143)

An important methodological question is the extent to which behavioral findings

from laboratory experiments are indeed representative of actual behavior in real traffic systems. 

The main conclusion from the comparative analyses was that behavioral mechanisms developed 

on the basis of laboratory experiments provided a good explanation of observed behavior, with 

essentially similar model specifications and correct signs but different coefficient magnitudes.

(143)

An extensive set of experiments was conducted by Mahmassani and colleagues to investigate user 

dynamics under real-time information of varying types. In contrast to earlier experiments, which 

addressed only the day-to-day dynamics of user decisions, the ATIS investigation addressed both 

real-time and day-to-day dynamics. As such, these experiments required a special purpose simulator 

that allowed real-time interaction between respondents and the traffic system. The interactive

simulator provided ATIS information that was consistent with the traffic conditions on the network.

The prevailing traffic conditions, in turn, were the result of collective decisions of individuals on 

the network, whose interactions in traffic were modeled using a dynamic traffic simulation model. 

Thus, the simulator ensured mutual consistency between user behavior, experienced traffic 

network conditions, and real-time information.

(144)

Three sets of experiments were performed over a 3-year period: (1) en-route path choice and 

day-to-day departure decisions under a given overall congestion level, (2) effect of congestion and 

experimental exposure sequence, and (3) effect of information type, quality, and feedback to users 

on user decision processes.(145,146)

An overview was presented by Mahmassani and Srinivasan.

(147)

In the past decade, interest has grown in the potential role of experimental economics approaches 

in travel behavior research. Methodologically, the following guidelines are generally followed in 

experimental economics:

(141)

• Use real monetary payoffs to incentivize subjects; in other words, the payoffs should be 

designed so as to induce the same behavioral response as the experienced consequences 

in a natural context.

• Publish complete experimental instructions.

• Do not use deception. There is considerable debate regarding this matter in the field; 

experimental evidence suggests that deception (false consequences to deny participants 

monetary payoffs) leads to unreliable responses and loss of goodwill.

• Avoid introducing specific, concrete context (i.e., keep the decision context stylized and 

generic and hence transferable and generalizable).
30

The precepts of experimental economics differ from prevailing practice in transportation and 

travel behavior research because the latter have generally sought to elicit responses to the actual 

attributes that influence choices in the real world, rather than some monetary surrogate that may 

be of questionable realism.

Selten et al. conducted laboratory experiments of a highly stylized day-to-day route choice game with

two route alternatives (a main road and a side road) and two experimental treatments corresponding 

to feedback about one’s own travel time and feedback about the travel times of the alternative route

in addition to one’s own route.(148)

Each experiment consisted of 18 players at a time (equilibrium 

consisted of 12 players on the main road and 6 on the side road). Methodologically, the payoffs 

increased according to a simple linear formula with decreasing travel time, itself related linearly to

volume. The researchers ran 200 iterations, considered a long time in experimental economics, but 

they still encountered large fluctuations. The results seemed to converge toward equilibrium but 

not perfectly, as fluctuations persisted under both treatments (fluctuations appeared to be smaller 

under the full-information treatment). Both direct and contrarian response modes could be identified

among the players, with direct players changing routes after a bad payoff and contrarians changing 

routes after a good payoff.

An important element in experimental economics that is of considerable relevance to travel 

behavior dynamics is the role of learning and judgment in repeated decision situations (e.g.,

day-to-day adjustment). Psychological studies have examined some of these questions through 

experiments on individual subjects but have typically ignored the effect of other decisionmakers 

and different information environments. Information availability plays an important role in 

determining which theories are feasible in different environments. Economists have investigated 

learning behavior both experimentally and theoretically but on a macroscopic scale, studying 

how simple information adjustment rules drive equilibrium processes in games under different 

information environments.

(149)

More recently, opportunities offered by online gaming environments have emerged as a promising 

approach for studying individual activity and travel choices.

(150) Activity and travel behavior in 

virtual environments is of interest for the following reasons:

• It is a manifestation of human behavior in a domain that is occupying a greater share of 

the time and resources of a growing segment of the population and increasingly cutting 

across social, demographic, and economic lines.

• Virtual world engagement is integrally linked to physical world behavior and, as such,

becomes essential in studying and predicting behavior in the latter.

• It is likely to provide insight into activity and travel behavior in the physical world and to 

help identify fundamental mechanisms underlying such behaviors.

• It may eventually provide a laboratory for observing behavior under controlled 

experimental conditions (e.g., in response to contemplated policies). 

However, designing games to address the questions of interest while retaining the players’

engagement remains a challenge.
31

CONCLUSIONS, RECOMMENDATIONS, AND FUTURE RESEARCH

This review reflects the diversity of travel behavior research. Nevertheless, a number of topics 

raised in many studies will shape the future of travel behavior research and the way we understand 

and model the mechanisms of travel behavior. These topics cut across all areas of analysis and 

understanding, from general knowledge to models and data, as each section cannot be used to draw 

conclusions by itself. It has been widely accepted that activity-based models are needed to model

the complexity of travel demand, and many activity-based models have been implemented. However,

most national travel surveys conduct travel diaries of 1 day, which account for neither the details 

desired to understand short-term behavior nor the extended time period to capture long-term effects.

Topics that should influence the nature of project methodology, data collection, and models include

behavior mechanisms, model integration, environmental impacts, and information in new data 

opportunities and how to use them. These appear in research quite often, but they have not been 

explored enough and are not ready to be implemented in today’s tools. The lack of general 

knowledge, difficulty in capturing and measuring behaviors, and complexity in modeling 

behavior are formidable challenges.

33

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