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A deeper investigation into the effect of the built environment on the use of ridehailing for non-work travel

Author

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  • Malik, Jai
  • Bunch, David S.
  • Handy, Susan
  • Circella, Giovanni

Abstract

Ridehailing has become a main-stream mobility option in many cities around the world. Many factors can influence an individual's decision to use ridehailing over other modes, and the growing need of policy makers to make built-environment and regulatory decisions related to ridehailing requires an increased understanding of these. This study develops a model that estimates how the built environment affects the decision to choose ridehailing for making non-work trips, while carefully accounting for a variety of confounding effects that could potentially bias the results (if ignored or improperly incorporated). These include: total number of trips, differences in supply between urban and non-urban areas, residential choice (e.g. urban versus non-urban areas), and household choice of whether to own a vehicle. We use individual-level data from a California travel survey that includes detailed attitude measurements to estimate an integrated choice and latent variable (ICLV) model to properly specify these effects. We include accessibility measures used elsewhere (e.g., Walkscore) plus measures developed for this study. Our analysis estimates the effect of these measures on ridehailing mode share, and how they differ between urban and non-urban areas. This analysis results in several major findings: we confirm that omission of latent preferences for residential location and vehicle ownership from the analysis can lead to biased results; previous studies may have overestimated the complementarity or substitution relationships between public transit and ridehailing by ignoring confounding effects; and even after accounting for other effects, individuals living in vibrant and walkable neighborhoods have a higher mode share for ridehailing (potentially using it instead of active modes).

Suggested Citation

  • Malik, Jai & Bunch, David S. & Handy, Susan & Circella, Giovanni, 2021. "A deeper investigation into the effect of the built environment on the use of ridehailing for non-work travel," Journal of Transport Geography, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:jotrge:v:91:y:2021:i:c:s0966692321000053
    DOI: 10.1016/j.jtrangeo.2021.102952
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    References listed on IDEAS

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    Cited by:

    1. Rico Krueger & Michel Bierlaire & Prateek Bansal, 2022. "A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections," Papers 2212.02178, arXiv.org.
    2. Sangwan Lee, 2022. "Exploring Associations between Multimodality and Built Environment Characteristics in the U.S," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
    3. Yan, Yingying & Zhong, Shiquan & Tian, Junfang & Jia, Ning, 2022. "An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak," Journal of Transport Geography, Elsevier, vol. 104(C).
    4. Hossain Mohiuddin & Md Musfiqur Rahman Bhuiya & Shaila Jamal & Zhi Chen, 2022. "Exploring the Choice of Bicycling and Walking in Rajshahi, Bangladesh: An Application of Integrated Choice and Latent Variable (ICLV) Models," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    5. Oded Cats & Rafal Kucharski & Santosh Rao Danda & Menno Yap, 2022. "Beyond the dichotomy: How ride-hailing competes with and complements public transport," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    6. Steven R. Gehrke & Michael P. Huff, 2024. "Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the phoenix metro region," Transportation, Springer, vol. 51(1), pages 271-295, February.

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