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How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco

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  • Basu, Rounaq
  • Sevtsuk, Andres

Abstract

This study adds to the nascent but growing literature on the use of big data for pedestrian route choice analysis. We explore behavioral preferences for various route attributes in Boston, MA using a large dataset of GPS trajectories (n = 11,165) sourced from a third-party smartphone app. Although the data are anonymized and limit our exploration of user heterogeneity, the sample size and area coverage are both much larger than seen in most previous studies. We estimate route choice preferences using a path size logit model, and operationalize the coefficients for policy-making through ‘willingness-to-walk’ measures. The value of these measures is demonstrated through an application of computing pedestrian accessibility to transit stations. Additionally, we compare our findings to a previous study in San Francisco, CA using similar data and methods, and previous literature to explore similarities and differences in pedestrian route choice behavior across major metropolitan areas more generally. While our findings can inform walkability policy and practice on several counts, we recommend future efforts to focus on supplementing this study by surveying hard-to-reach populations for more equitable policy-making.

Suggested Citation

  • Basu, Rounaq & Sevtsuk, Andres, 2022. "How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and San Francisco," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 1-19.
  • Handle: RePEc:eee:transa:v:163:y:2022:i:c:p:1-19
    DOI: 10.1016/j.tra.2022.06.007
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    References listed on IDEAS

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    1. Basu, Rounaq & Ferreira, Joseph, 2021. "Sustainable mobility in auto-dominated Metro Boston: Challenges and opportunities post-COVID-19," Transport Policy, Elsevier, vol. 103(C), pages 197-210.
    2. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
    3. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," The Journal of Business, University of Chicago Press, vol. 53(3), pages 13-29, July.
    4. Guo, Zhan & Loo, Becky P.Y., 2013. "Pedestrian environment and route choice: evidence from New York City and Hong Kong," Journal of Transport Geography, Elsevier, vol. 28(C), pages 124-136.
    5. Sevtsuk, Andres & Basu, Rounaq, 2022. "The role of turns in pedestrian route choice: A clarification," Journal of Transport Geography, Elsevier, vol. 102(C).
    6. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Shortest path distance vs. least directional change: Empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, Elsevier, vol. 74(C), pages 37-52.
    7. repec:hal:wpaper:hal-03168957 is not listed on IDEAS
    8. Glaser, Meredith & Krizek, Kevin J., 2021. "Can street-focused emergency response measures trigger a transition to new transport systems? Exploring evidence and lessons from 55 US cities," Transport Policy, Elsevier, vol. 103(C), pages 146-155.
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    Cited by:

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    2. Yuki Oyama, 2023. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes," Papers 2307.08646, arXiv.org.
    3. Sevtsuk, Andres & Basu, Rounaq, 2022. "The role of turns in pedestrian route choice: A clarification," Journal of Transport Geography, Elsevier, vol. 102(C).
    4. Natalia Distefano & Salvatore Leonardi & Nilda Georgina Liotta, 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    5. Oyama, Yuki, 2024. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of visually perceived attributes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    6. Sander van Cranenburgh & Francisco Garrido-Valenzuela, 2023. "Computer vision-enriched discrete choice models, with an application to residential location choice," Papers 2308.08276, arXiv.org.
    7. Duncan, Michael, 2023. "The influence of pedestrian plans on walk commuting in US municipalities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).

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