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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 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 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 m2) 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 stated-preference (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.


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