The role of built environment in shaping reserved ride-hailing services: Insights from interpretable machine learning approach

Cruising ride-hailing vehicles exacerbate traffic congestion by generating negative externalities. In contrast, reserved ride-hailing services leverage precise information regarding the departure times and origins-destinations of future trips. Platforms can use this data to dispatch and route drivers more efficiently, thereby reducing the need for cruising. Although previous research has largely concentrated on real-time ride-hailing services, the impact of the built environment on reserved ride-hailing remains unexplored with empirical data. This study integrates multi-source data from Haikou City in China and utilizes the gradient boosting decision tree model, which is an interpretable machine learning approach, to investigate potential relationships between reserved ride-hailing trip demand and the built environment. The rankings of relative importance reveal that factors such as the density of food services, education institutions, accessibility to town centers, and proximity to transportation hubs significantly influence the demand for reserved ride-hailing. Furthermore, the study demonstrates that the aforementioned factors exhibit non-linear effects on the demand for reserved ride-hailing. The findings have policy implications for local governments aiming to promote reserved ride-hailing and enhance urban mobility services.

• Record URL:
  • https://doi.org/10.1016/j.rtbm.2024.101173
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2210539524000750
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22105395
• 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:
  • Li, Wu
  • Ma, Jingwen
  • Cai, Haiming
  • Chen, Fang
  • Qin, Wenwen
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 101173
• Serial:
  • Research in Transportation Business & Management
  • Volume: 56
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-5395
  • Serial URL: http://www.sciencedirect.com/science/journal/22105395

Subject/Index Terms

• TRT Terms: Built environment; Ridesourcing; Traffic congestion
• Geographic Terms: Haikou Shi (China)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01926102
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 30 2024 3:58PM