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Estimating Pedestrian Flows on Street Networks

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  • Andres Sevtsuk

Abstract

Problem, research strategy, and findingsCity governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts.Takeaway for practiceThe proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic.

Suggested Citation

  • Andres Sevtsuk, 2021. "Estimating Pedestrian Flows on Street Networks," Journal of the American Planning Association, Taylor & Francis Journals, vol. 87(4), pages 512-526, October.
  • Handle: RePEc:taf:rjpaxx:v:87:y:2021:i:4:p:512-526
    DOI: 10.1080/01944363.2020.1864758
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    Cited by:

    1. Andres Sevtsuk & Rounaq Basu & Bahij Chancey, 2021. "We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in Melbourne," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.
    2. Haiyan Hao & Yan Wang, 2024. "A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters," Papers 2401.04849, 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. Winston Yap & Jiat-Hwee Chang & Filip Biljecki, 2023. "Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions," Environment and Planning B, , vol. 50(6), pages 1416-1437, July.
    5. An, Zihao & Xie, Bo & Liu, Qiyang, 2023. "No street is an Island: Street network morphologies and traffic safety," Transport Policy, Elsevier, vol. 141(C), pages 167-181.
    6. Wali, Behram & Frank, Lawrence D., 2024. "Redefining walkability to capture safety: Investing in pedestrian, bike, and street level design features to make it safe to walk and bike," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).

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