Advanced Search
MyIDEAS: Login to save this paper or follow this series

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

Contents:

Author Info

  • Schneider, Robert James
Registered author(s):

    Abstract

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.escholarship.org/uc/item/06v2g6dh.pdf;origin=repeccitec
    Download Restriction: no

    Bibliographic Info

    Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt06v2g6dh.

    as in new window
    Length:
    Date of creation: 01 May 2011
    Date of revision:
    Handle: RePEc:cdl:uctcwp:qt06v2g6dh

    Contact details of provider:
    Postal: 109 McLaughlin Hall, Mail Code 1720, Berkeley, CA 94720-1720
    Phone: 510-642-3585
    Fax: 510-643-3955
    Email:
    Web page: http://www.escholarship.org/repec/uctc/
    More information through EDIRC

    Related research

    Keywords: Social and Behavioral Sciences;

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521747387.
    2. Carlo Ratti & Riccardo Maria Pulselli & Sarah Williams & Dennis Frenchman, 2006. "Mobile Landscapes: using location data from cell phones for urban analysis," Environment and Planning B: Planning and Design, Pion Ltd, London, Pion Ltd, London, vol. 33(5), pages 727-748, September.
    3. Sungyop Kim & Gudmundur Ulfarsson, 2008. "Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips," Transportation, Springer, Springer, vol. 35(6), pages 723-737, November.
    4. Mokhtarian, Patricia L. & Salomon, Ilan, 2001. "How derived is the demand for travel? Some conceptual and measurement considerations," Transportation Research Part A: Policy and Practice, Elsevier, Elsevier, vol. 35(8), pages 695-719, September.
    5. Bhat, Chandra R. & Gossen, Rachel, 2004. "A mixed multinomial logit model analysis of weekend recreational episode type choice," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 38(9), pages 767-787, November.
    6. Ryley, Timothy John, 2008. "The propensity for motorists to walk for short trips: Evidence from West Edinburgh," Transportation Research Part A: Policy and Practice, Elsevier, Elsevier, vol. 42(4), pages 620-628, May.
    7. Thirayoot Limanond & Debbie Niemeier, 2004. "Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA," Transportation, Springer, Springer, vol. 31(2), pages 153-181, May.
    8. Rose, Geoff & Marfurt, Heidi, 2007. "Travel behaviour change impacts of a major ride to work day event," Transportation Research Part A: Policy and Practice, Elsevier, Elsevier, vol. 41(4), pages 351-364, May.
    9. Roger Mackett, 2003. "Why do people use their cars for short trips?," Transportation, Springer, Springer, vol. 30(3), pages 329-349, August.
    10. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, Springer, vol. 30(2), pages 133-176, May.
    11. Liang Long & Jie Lin & Kimon Proussaloglou, 2010. "Investigating Contextual Variability in Mode Choice in Chicago Using a Hierarchical Mixed Logit Model," Urban Studies, Urban Studies Journal Limited, Urban Studies Journal Limited, vol. 47(11), pages 2445-2459, October.
    12. Xinyu Cao & Susan Handy & Patricia Mokhtarian, 2006. "The Influences of the Built Environment and Residential Self-Selection on Pedestrian Behavior: Evidence from Austin, TX," Transportation, Springer, Springer, vol. 33(1), pages 1-20, 01.
    13. Schneider, Robert J. & Arnold, Lindsay S. & Ragland, David R., 2009. "A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings, Institute of Transportation Studies, UC Berkeley qt3nr8h66j, Institute of Transportation Studies, UC Berkeley.
    14. David Hensher & April Reyes, 2000. "Trip chaining as a barrier to the propensity to use public transport," Transportation, Springer, Springer, vol. 27(4), pages 341-361, December.
    15. Cynthia Chen & Hongmian Gong & Robert Paaswell, 2008. "Role of the built environment on mode choice decisions: additional evidence on the impact of density," Transportation, Springer, Springer, vol. 35(3), pages 285-299, May.
    16. Philip A. Viton, 2004. "Will Mixed Logit Change Urban Transport Policies?," Journal of Transport Economics and Policy, London School of Economics and University of Bath, London School of Economics and University of Bath, vol. 38(3), pages 403-423, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Schneider, Robert James, 2013. "Measuring transportation at a human scale: An intercept survey approach to capture pedestrian activity," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, Center for Transportation Studies, University of Minnesota, vol. 6(3), pages 43-59.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:cdl:uctcwp:qt06v2g6dh. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lisa Schiff).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.