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Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration

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  • Yang Yang
  • Samitha Samaranayake
  • Timur Dogan

Abstract

This paper introduces a joint choice model for travel mode and duration to quantify the mobility impacts of urban design changes on the built environment. The model is formulated as a Random Forest classifier that predicts the mode-duration probabilities of a given trip. A novel series of predictor features are proposed which measure the urban form, demographics, and service densities on different scales of the transportation network. Through a sensitivity analysis and a proof-of-concept case study, we find that a dense, mixed-use environment with good coverage of a multi-modal mobility network can significantly promote active transportation and public transit use. However, we also find that ultra-dense, centralized developments can lead to increased travel time and increased vehicle use in the urban periphery. Our modeling and analysis method provides a simplified and effective way to assess urban design and planning scenarios from different mobility perspectives and facilitates data-driven, mobility-aware urban design and planning that can help identify better solutions more quickly.

Suggested Citation

  • Yang Yang & Samitha Samaranayake & Timur Dogan, 2023. "Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration," Environment and Planning B, , vol. 50(9), pages 2359-2375, November.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:9:p:2359-2375
    DOI: 10.1177/23998083231154263
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    References listed on IDEAS

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    1. Carlos Moreno & Zaheer Allam & Didier Chabaud & Catherine Gall & Florent Pratlong, 2021. "Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities," Post-Print hal-03549665, HAL.
    2. De Vos, Jonas & Cheng, Long & Kamruzzaman, Md. & Witlox, Frank, 2021. "The indirect effect of the built environment on travel mode choice: A focus on recent movers," Journal of Transport Geography, Elsevier, vol. 91(C).
    3. Chaoying Yin & Xiaoquan Wang & Chunfu Shao & Jianxiao Ma, 2022. "Exploring the Relationship between Built Environment and Commuting Mode Choice: Longitudinal Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    4. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
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