Joint Order Dispatching and Vehicle Repositioning for Dynamic Ridesharing

Zhidan Liu, Guofeng Ouyang, Bolin Zhang, Bo Du, Chao Chen, Kaishun Wu

Abstract: Dynamic ridesharing has gained significant attention in recent years. However, existing ridesharing studies often focus on optimizing order dispatching and vehicle repositioning separately, leading to short-sighted decisions and underutilization of the ridesharing potential. In this paper, we propose a novel joint optimization framework called $\mathtt {JODR}$. By coordinating order dispatching and vehicle repositioning, $\mathtt {JODR}$ enhances ridesharing efficiency while ensuring high-quality service. The core idea of $\mathtt {JODR}$ is to dispatch ride orders with high demand in specific mobility directions to vehicles with sufficient available capacity, effectively balancing future supply and demand in those directions. To achieve this, we introduce a novel mobility value function that can predict the long-term mobility value of matching an order with its travel direction. By considering orders’ directional mobility values, service quality assessments, and available vehicle capacities, $\mathtt {JODR}$ formulates the order dispatching as a minimum-cost maximum-flow problem to derive the optimal order-vehicle assignments. Furthermore, the value function helps the intelligent repositioning of idle vehicles. Extensive experiments conducted on a large real-world dataset demonstrate the superiority of $\mathtt {JODR}$ over state-of-the-art methods across various performance metrics. These experimental results validate the effectiveness of $\mathtt {JODR}$ in improving the ridesharing efficiency and experience.

External IDs:dblp:journals/tmc/LiuOZDCW25