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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

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 household- and 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 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 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.


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