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.

本研究提出了一种创新的基于模型的模块化方法（MMA），以动态优化拼车平台中的订单匹配和车辆重定位。MMA利用了一个两层和模块化的建模结构，其上层确定了系统内车流的空间转移模式，以最大化当前和未来阶段的总收益。在上层的指导下，下层执行快速的车辆-订单匹配和车辆重定位。我们理论上证明了所提算法可以在标准化网络中实现全局最优，而基于玩具网络和实际数据集的数值实验表明，与批量匹配和基于强化学习的方法相比，MMA能够实现更优越的系统性能。此外，其模块化和轻量级的建模结构使其能够在需求变化较大的情况下，具有较高的鲁棒性，并保持相对较低的计算成本。

网上移动定位与匹配的顺风车服务：基于模型的模块化方法