Optimizing matching time intervals for ride-hailing services using reinforcement learning

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.

Automated summary

Efficiently matching trip requests and available drivers in ride-hailing services is crucial, with a strategy of delaying matching to improve efficiency. The optimal delayed matching depends on the trade-off between delay penalty and reduced wait cost, and tailored reinforcement learning-based methods are used to overcome challenges. This work also addresses spatial partitioning balance and provides insights into delayed matching policies with real-world taxi trajectory data.