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

Yisheng Peng, Jiahui Liu, Fangyou Li, Jianqiang Cui, Yi Lu and Linchuan Yang

Journal of Transport Geography, 2024, vol. 120, issue C

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.

Keywords: Ride-hailing; Human mobility resilience; Air quality; Physical environment; Gradient boosting decision tree (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:120:y:2024:i:c:s0966692324001807

DOI: 10.1016/j.jtrangeo.2024.103971

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