This study proposes an innovative model-based modular approach (MMA) to dynamically optimize order matching and vehicle relocation in a ride-hailing platform. MMA utilizes a two-layer and modular modeling structure. The upper layer determines the spatial transfer patterns of vehicle flow within the system to maximize the total revenue of the current and future stages. With the guidance provided by the upper layer, the lower layer performs rapid vehicle-to-order matching and vehicle relocation. MMA is interpretable, and equipped with the customized and polynomial-time algorithm, which, as an online order-matching and vehicle-relocation algorithm, can scale past thousands of vehicles. We theoretically prove that the proposed algorithm can achieve the global optimum in stylized networks, while the numerical experiments based on both the toy network and realistic dataset demonstrate that MMA is capable of achieving superior systematic performance compared to batch matching and reinforcement-learning based methods. Moreover, its modular and lightweight modeling structure further enables it to achieve a high level of robustness against demand variation while maintaining a relatively low computational cost.

翻译：本研究提出了一种创新的基于模型的模块化方法（MMA），用于动态优化网约车平台的订单匹配与车辆调度。MMA采用双层模块化建模结构：上层确定系统内车辆流的空间转移模式，以最大化当前与未来阶段的总体收益；下层在上层指导下快速执行车辆与订单的匹配及车辆调度。MMA具有可解释性，并配备了定制化的多项式时间算法。作为在线订单匹配与车辆调度算法，其可扩展至数千辆车辆规模。我们理论证明了该算法能在典型化网络中获得全局最优解，而基于模拟网络与现实数据集的数值实验表明，与批量匹配及基于强化学习的方法相比，MMA能实现更优的系统性能。此外，其模块化轻量建模结构在保持较低计算成本的同时，能有效提升对需求波动的鲁棒性。