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

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. The authors 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.

• Record URL:
  • https://doi.org/10.1016/j.jtrangeo.2024.103971
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0966692324001807
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Peng, Yisheng
  • Liu, Jiahui
  • Li, Fangyou
  • Cui, Jianqiang
  • Lu, Yi
  • Yang, Linchuan
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103971
• Serial:
  • Journal of Transport Geography
  • Volume: 120
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0966-6923
  • Serial URL: http://www.elsevier.com/locate/jtrangeo

Subject/Index Terms

• TRT Terms: Air pollution; Built environment; Mobility; Ridesourcing; Socioeconomic factors; Travel behavior
• Geographic Terms: Chengdu (China)
• Subject Areas: Environment; Highways; Passenger Transportation; Planning and Forecasting; Society;

Filing Info

• Accession Number: 01932256
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2024 8:43AM