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The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

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  • Haitao Yu

    (International Center for Adaptation Planning and Design (iAdapt), College of Design, Construction, and Planning, University of Florida, USA)

  • Zhong-Ren Peng

    (International Center for Adaptation Planning and Design (iAdapt), College of Design, Construction, and Planning, University of Florida, USA; China Institute for Urban Governance, Shanghai Jiao Tong University, China)

Abstract

Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.

Suggested Citation

  • Haitao Yu & Zhong-Ren Peng, 2020. "The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas," Urban Studies, Urban Studies Journal Limited, vol. 57(1), pages 152-175, January.
    • Handle: RePEc:sae:urbstu:v:57:y:2020:i:1:p:152-175
    DOI: 10.1177/0042098019828180
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    3. 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.
    4. Qiao, Si & Yeh, Anthony Gar-On, 2021. "Is ride-hailing a valuable means of transport in newly developed areas under TOD-oriented urbanization in China? Evidence from Chengdu City," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. Dean, Matthew D. & Kockelman, Kara M., 2021. "Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago," Journal of Transport Geography, Elsevier, vol. 91(C).
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    7. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
    8. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.
    9. Timmer, Sebastian & Bösehans, Gustav & Henkel, Sven, 2023. "Behavioural norms or personal gains? – An empirical analysis of commuters‘ intention to switch to multimodal mobility behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
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    12. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).

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