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Understanding Sustainable Transportation Choices: Shifting Routine Automobile Travel to Walking and Bicycling


In the two decades since the United States Congress passed the federal Intermodal Surface Transportation Efficiency Act, there has been a surge of interest in making urban transportation systems more sustainable. Many agencies, representing all levels of government, have searched for strategies to reduce private automobile use, including policies to shift local driving to pedestrian and bicycle modes. Progress has been made in a number of communities, but the automobile remains the dominant mode of transportation in all metropolitan regions.

Sustainable transportation advocates are especially interested in routine travel, such as shopping and other errands, because it tends to be done frequently and for distances that could be covered realistically by walking or bicycling. According to the 2009 National Household Travel Survey, Americans made more trips for shopping than for any other purpose, including commuting to and from work. One-third of these shopping trips were shorter than two miles (3.2 km). However, 76% of these short shopping trips were made by automobile, while only 21% were made by walking and 1% by bicycling.

In order to identify effective strategies to change travel behavior, practitioners need a greater understanding of why people choose certain modes for routine travel. Choosing to walk or bicycle rather than travel by automobile may help individuals get exercise, save money, interact with neighbors, and reduce tailpipe emissions. Yet, in some communities, non-motorized modes may also require more time and physical effort to run a series of errands, be less convenient for carrying packages and traveling in bad weather, and be perceived as having a higher risk of traffic crashes or street crime than driving.

A mixed-methods approach was used to develop a more complete understanding of factors that are associated with walking or bicycling rather than driving for routine travel. An intercept survey was implemented to gather travel data from 1,003 customers at retail pharmacy stores in 20 San Francisco Bay Area neighborhoods in fall 2009. Follow-up interviews were conducted with 26 survey participants in spring and summer 2010 to gain a deeper understanding of factors that influenced their transportation decisions.

The methodological approach makes several contributions to the body of research on sustainable transportation. For example, the study: Explored multiple categories of factors that may be associated with walking and bicycling, including travel, socioeconomic, attitude, perception, and shopping district characteristics. Few studies of pedestrian or bicycle mode choices have included all of these categories of factors. Statistical models showed that variables in all categories had significant associations with mode choice. Documented and analyzed short pedestrian movements, such as from a parking space to a store entrance or from a bus stop to home. These detailed data provided a greater understanding of pedestrian activity than traditional travel survey analyses. Walking was used as the primary mode for 65% of respondent trips between stops within shopping districts, and 52% of all respondents walked along a street or between stops at some time between leaving and returning home. Maps of respondent pedestrian path density revealed distinct pedestrian activity patterns in different types of shopping districts. Used four different approaches to capture participant travel mode information. Respondents reported the primary mode of transportation they were using on the day of the survey, the mode they typically used, and all modes that they would consider using to travel to the survey store. They also mapped all stops on their tour and said what modes they used between each stop. These four approaches revealed nuanced travel habits and made it possible to correct inaccuracies in self-reported primary travel mode data. Measured and tested fine-grained local environment variables in shopping districts rather than around respondents' homes. These variables characterized the shopping district area (e.g., sidewalks, bicycle facilities, metered parking, and tree canopy coverage), the main commercial roadway (e.g., posted speed limit, number of automobile lanes, and pedestrian crossing distance), and the survey store site (e.g., number of automobile and bicycle parking spaces and distance from the public sidewalk to the store entrance). This dissertation adds to the small number of studies that have explored how the characteristics of activity destinations are related to travel behavior.

The study results contribute to the body of knowledge about factors that may encourage people to shift routine travel from automobile to pedestrian or bicycle modes. After controlling for travel factors such as time and cost, socioeconomic characteristics, and individual attitudes, mixed logit models showed that automobile use was negatively associated with higher employment density, smaller parking lots, and metered on-street parking in the shopping district. Walking was positively associated with higher population density, more street tree canopy coverage, lower speed limits, and fewer commercial driveway crossings. The exploratory analysis of a small number of bicycle tours found that bicycling was associated with more extensive bicycle facility networks and more bicycle parking. However, people were more likely to drive when they perceived a high risk of crime.

Results also suggest the magnitude of mode shifts that could occur if short- and long-term land use and transportation system changes were made to each study shopping district. The mode choice model representing travel only to and from the study shopping districts (N = 388) was used to estimate respondent mode shares under the following three scenarios: 1) double population and employment densities in each study shopping district, 2) double street tree canopy coverage in each study shopping district, and 3) eliminate half of the automobile parking 3 spaces at the survey store. Based on the model, the combination of these three changes could increase pedestrian mode share among the 388 sample respondents from 43% to 61% and decrease automobile mode share from 50% to 31%. This shift could eliminate 129 (13%) of the 983 respondent vehicle miles traveled (208 of the 1,580 respondent vehicle kilometers traveled), and 110 (36%) of the 308 times respondents parked their automobiles in the shopping district.

The mode choice model of walking versus driving within survey shopping districts (N = 286) was used to test the combination of the following scenarios: 1) cluster separated stores around shared parking lots, 2) consolidate commercial driveways so that there are half as many driveway crossings along the main commercial roadway, 3) reduce all main commercial roadway speed limits to 25 miles per hour (40 kilometers per hour), and 4) install metered parking in all shopping districts. These changes could increase the percentage of the 286 sample respondents walking between shopping district activities from 32% to 54%. This shift could eliminate 29 (38%) of the 76 respondent vehicle miles traveled (47 of the 122 respondent vehicle kilometers traveled), and 105 (22%) of the 469 times respondents parked their automobiles in the shopping district. Note that these forecasted mode shifts are illustrative examples based on cross-sectional data and do not account for the process of modifying travel behavior habits.

Qualitative interviews provided a foundation for a proposed Theory of Routine Mode Choice Decisions. This five-step theory also drew from survey results and other mode choice theories in the transportation and psychology fields. The first step, 1) awareness and availability, determines which modes are viewed as possible choices for routine travel. The next three steps, 2) basic safety and security, 3) convenience and cost, and 4) enjoyment, assess situational tradeoffs between modes in the choice set and are supported by many of the statisticallysignificant factors in the mode choice models. The final step, 5) habit, reinforces previous choices and closes the decision process loop. Socioeconomic characteristics explain differences in how individuals view each step in the process. Understanding each step in the mode choice decision process can help planners, designers, engineers, and other policy-makers implement a comprehensive set of strategies that may be able to shift routine automobile travel to pedestrian and bicycle modes.

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