Abstract This study investigates a flexible one-to-two matching problem in ride-hailing systems. In the ride-hailing platform, dedicated drivers are assigned to serve a single request (one-to-one matching) or two requests (one-to-two matching). We aim to examine the en-route matching problem, i.e., an in-transit driver who is currently serving riders may be informed to pick up co-riders traveling in the same direction. We consider boundedly rational users who accept ride-shares at reasonable travel costs. To encourage riders to join one-to-two matching, we propose an incentive scheme under which riders who experience detour time in one-to-two matching will be charged at a discounted rate. To make en-route decisions quickly, we propose a rolling horizon approach. The flexible one-to-two matching problem is formulated as a multi-stage integer program with the objective of maximizing the system-wide profit. We derive the upper bound on detour time for riders, based on which we develop an efficient algorithm to determine cost-feasible ride-shares. We also develop a neighborhood searching method to identify the locations that are close to a driver. Exploiting the historical trip data of New York taxis, we investigate the matching patterns and provide strategies to benefit the users and the platform when the ride-hailing program is introduced into the existing road network. The numerical experiments reveal that our proposed incentive scheme for one-to-two matching problems can increase both ridership and profit. Furthermore, the productivity and the compensation of dedicated drivers are also improved.
Highlights Develop a multi-stage optimization model for matching between riders and drivers Propose an incentive scheme to ensure the fairness of riders experiencing detours. Derive the upper bound on detour time for boundedly rational riders. Evaluated the effectiveness of the proposed incentive scheme using real-world data. More ride-shares are mathced if riders’ aspiration level is high.
Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users
2021-03-28
Article (Journal)
Electronic Resource
English
Ride-matching for the ride-hailing platform with heterogeneous drivers
Elsevier | 2023
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