Dynamic matching radius decision model for on-demand ride services: A deep multi-task learning approach

As ride-hailing services have experienced significant growth, most research has concentrated on the dispatching mode, where drivers must accept the platform’s assigned trip requests. However, the broadcasting mode, in which drivers can freely choose their preferred orders from those broadcast by the platform, has received less attention. One crucial but challenging task in such a system is the determination of the matching radius, which usually varies across space, time, and real-time supply/demand characteristics. This study develops a Deep Learning-based Matching Radius Decision (DL-MRD) model that predicts key system performance metrics for a range of matching radii, which enables the ride-hailing platform to select an optimal matching radius that maximizes overall system performance according to real-time supply and demand information. To simultaneously maximize multiple system performance metrics for matching radius determination, the authors devise a novel multi-task learning algorithm named Weighted Exponential Smoothing Multi-task (WESM) learning strategy that enhances convergence speed of each task (corresponding to the optimization of one metric) and delivers more accurate overall predictions. They evaluate their methods in a simulation environment designed for broadcasting-mode-based ride-hailing service. Their findings reveal that dynamically adjusting matching radii based on their proposed approach significantly improves system performance.

• Record URL:
  • https://doi.org/10.1016/j.tre.2024.103822
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1366554524004137
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• 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:
  • Chen, Taijie
  • Shen, Zijian
  • Feng, Siyuan
  • 0000-0002-3194-1124
  • Yang, Linchuan
  • Ke, Jintao
• Publication Date: 2025-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 103822
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 193
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Broadcasting; Dynamic models; Machine learning; Radius; Ridesourcing; Travel demand
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01941659
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 31 2024 4:31PM