Journal article
Optimizing matching time intervals for ride-hailing services using reinforcement learning
Transportation research. Part C, Emerging technologies, Vol.129, p.103239
08/01/2021
DOI: 10.1016/j.trc.2021.103239
Abstract
Matching trip requests and available drivers efficiently is considered a central operational problem for ride-hailing services. A widely adopted matching strategy is to accumulate a batch of potential passenger-driver matches and solve bipartite matching problems repeatedly. The efficiency of matching can be improved substantially if the matching is delayed by adaptively adjusting the matching time interval. The optimal delayed matching is subject to the trade-off between the delay penalty and the reduced wait cost and is dependent on the system's supply and demand states. Searching for the optimal delayed matching policy is challenging, as the current policy is compounded with past actions. To this end, we tailor a family of reinforcement learning-based methods to overcome the curse of dimensionality and sparse reward issues. In addition, this work provides a solution to spatial partitioning balance between the state representation error and the optimality gap of asynchronous matching. Lastly, we examine the proposed methods with real-world taxi trajectory data and garner managerial insights into the general delayed matching policies. The focus of this work is single-ride service due to limited access to shared ride data, while the general framework can be extended to the setting with a ridepooling component.
Details
- Title: Subtitle
- Optimizing matching time intervals for ride-hailing services using reinforcement learning
- Creators
- Guoyang Qin - Tongji UniversityQi Luo - Clemson UniversityYafeng Yin - University of Michigan–Ann ArborJian Sun - Tongji UniversityJieping Ye - Art Institute of Wisconsin
- Resource Type
- Journal article
- Publication Details
- Transportation research. Part C, Emerging technologies, Vol.129, p.103239
- Publisher
- Elsevier
- DOI
- 10.1016/j.trc.2021.103239
- ISSN
- 0968-090X
- eISSN
- 1879-2359
- Number of pages
- 18
- Grant note
- 201806260144 / China Scholarship Council (CSC); China Scholarship Council CMMI-1854684; CMMI-1904575 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 08/01/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696716202771
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