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Coordinating, Developing, and Delivering Highway Transportation Innovations

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Publication Number:  FHWA-HRT-13-022    Date:  August 2013
Publication Number: FHWA-HRT-13-022
Date: August 2013


Synthesis of Traveler Choice Research: Improving Modeling Accuracy for Better Transportation Decisionmaking

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 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 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 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.


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