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Measuring transportation at a human scale: An intercept survey approach to capture pedestrian activity

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  • Robert James Schneider

    (University of Wisconsin-Milwaukee)

Abstract

Pedestrian travel data are critical for measuring and analyzing sustainable transportation systems. However, traditional household travel surveys and analysis methods often ignore secondary modes, such as walking from a street parking space to a store entrance or walking from a bus stop to home. New data collection and analysis techniques are needed, especially in areas where walking is common. This paper describes an intercept survey methodology used to measure retail pharmacy customer travel to, from, and within 20 shopping districts in the San Francisco Bay Area. Of the 1003 respondents, 959 (96 percent) reported all modes of travel used from leaving home until returning home, including secondary modes. Walking was the primary travel mode on 21 percent of respondent tours, but an analysis of secondary modes found that 52 percent of tours included some walking. Pedestrian travel was particularly common within shopping districts, accounting for 65 percent of all trips within 804 meters (0.5 miles) of survey stores. Detailed walking path data from the survey showed that respondents in denser, more mixed-use shopping districts tended to walk along the main commercial street as well as other streets connecting to the core shopping area, while respondent pedestrian movements in automobile-oriented shopping districts tended to be contained within specific shopping complexes.

Suggested Citation

  • Robert James Schneider, 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, vol. 6(3), pages 43-59.
  • Handle: RePEc:ris:jtralu:0113
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    References listed on IDEAS

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    3. Schneider, Robert James, 2011. "Understanding Sustainable Transportation Choices: Shifting Routine Automobile Travel to Walking and Bicycling," University of California Transportation Center, Working Papers qt06v2g6dh, University of California Transportation Center.
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    Cited by:

    1. Marilyn E Wende & S Morgan Hughey & Alexander C McLain & Shirelle Hallum & J Aaron Hipp & Jasper Schipperijn & Ellen W Stowe & Andrew T Kaczynski, 2024. "Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-18, April.
    2. Minaei, Negin, 2014. "Do modes of transportation and GPS affect cognitive maps of Londoners?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 162-180.
    3. Robert J. Schneider & Lingqian Hu & Joseph Stefanich, 2019. "Development of a neighborhood commute mode share model using nationally-available data," Transportation, Springer, vol. 46(3), pages 909-929, June.

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    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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