Ride-Sharing Matching under Travel Time Uncertainty through Data-Driven Robust Optimization

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Li, Xiaoming, Gao, Jie, Wang, Chun, Huang, Xiao, Nie, Yimin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Costs; Data models; Data-driven robust optimization; Delay effects; Design optimization; gated recurrent units; Heuristic algorithms; Heuristic methods; Machine learning; Matching; mobility-on-demand; Optimization; Performance evaluation; Performance measurement; Predictive models; ride-sharing matching; Robustness (mathematics); Route planning; Stochastic processes; time-series prediction; Traffic information; Travel time; Uncertainty; Vehicle routing; Vehicles; Windows (intervals)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3218700

In ride-sharing services, travel time uncertainty significantly impacts the quality of matching solutions for both the drivers and the riders. This paper studies a one-to-many ride-sharing matching problem where travel time between locations is uncertain. The goal is to generate robust ride-sharing matching solutions that minimize the total driver detour cost and the number of unmatched riders. To this end, we formulate the ride-sharing matching problem as a robust vehicle routing problem with time window (RVRPTW). To effectively capture the travel time uncertainty, we propose a deep learning-based data-driven approach that can dynamically estimate the uncertainty sets of travel times. Given the NP-hard nature of the optimization problem, we design a hybrid meta-heuristic algorithm that can handle large-scale instances in a time-efficient manner. To evaluate the performance of the proposed method, we conduct a set of numeric experiments based on real traffic data. The results confirm that the proposed approach outperforms the non-data-driven one in several important performance metrics, including a proper balance between robustness and inclusiveness of the matching solution. Specifically, by applying the proposed data-driven approach, the matching solution violation rate can be reduced up to 85.8%, and the valid serving rate can be increased up to 42.3% compared to the non-data-driven benchmark.