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Resilience of ride-hailing services in response to air pollution and its association with built-environment and socioeconomic characteristics

Author

Listed:
  • Peng, Yisheng
  • Liu, Jiahui
  • Li, Fangyou
  • Cui, Jianqiang
  • Lu, Yi
  • Yang, Linchuan

Abstract

Air pollution, an unexpected event, poses a significant threat to public health and affects human mobility. Ride-hailing provides an effective way to understand how human mobility adapts to air pollution. This study examines a week-long ride-hailing demand dataset from Chengdu, China, to evaluate the resilience of ride-hailing services (or ride-hailing resilience) in the face of poor air quality. A gradient boosting decision tree model is developed to explore the non-linear and interaction effects of air pollution, the built environment, and socioeconomic characteristics on ride-hailing demand and resilience. The results show that the relative importance and impact of independent factors on ride-hailing demand and resilience vary. Specifically, the density of residence facilities and air pollution are the most important predictors of ride-hailing demand and resilience, respectively. The non-linear and interaction effects of air pollution and selected built-environment and socioeconomic characteristics on ride-hailing resilience are presented. We recommend that urban planners and policymakers address the vulnerability of regions to air pollution, optimize the allocation of ride-hailing resources, and develop strategies to improve regional resilience.

Suggested Citation

  • Peng, Yisheng & Liu, Jiahui & Li, Fangyou & Cui, Jianqiang & Lu, Yi & Yang, Linchuan, 2024. "Resilience of ride-hailing services in response to air pollution and its association with built-environment and socioeconomic characteristics," Journal of Transport Geography, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jotrge:v:120:y:2024:i:c:s0966692324001807
    DOI: 10.1016/j.jtrangeo.2024.103971
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