A Learning Method for Real-Time Repositioning in E-Hailing Services

Internet-based ride-sourcing platforms have become an important component of urban transportation systems in recent years. The spatial-temporal mismatch of supply (available vehicles) and demand (passenger requests) deteriorates the performance of ride-sourcing services. Hence, repositioning available vehicles to service unserved requests can be effective. In this paper, we propose a real-time repositioning method that considers both the responsiveness to immediate demand and the long-term operation efficiency simultaneously. The proposed method is achieved by integrating the solutions of two procedures: i) A singleagent Markov Decision Process (MDP) model is employed to evaluate the long-term influence of the repositioning on platform efficiency. ii) A binary nonlinear programming (BNLP) is introduced to model the multi-driver repositioning problem in real-time, which is solved by an Iterative Edge Cutting (IEC) algorithm. Numerical experiments demonstrate that the proposed method significantly outperforms several repositioning benchmarks regarding both platform efficiency, including servicing more orders and reducing order cancellations, and users’ experience including reducing passengers’ waiting time and increasing drivers’ occupied rate.