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This study proposes an innovative model-based modular approach (MMA) todynamically optimize order matching and vehicle relocation in a ride-hailingplatform. MMA utilizes a two-layer and modular modeling structure. The upperlayer determines the spatial transfer patterns of vehicle flow within thesystem to maximize the total revenue of the current and future stages. With theguidance provided by the upper layer, the lower layer performs rapidvehicle-to-order matching and vehicle relocation. MMA is interpretable, andequipped with the customized and polynomial-time algorithm, which, as an onlineorder-matching and vehicle-relocation algorithm, can scale past thousands ofvehicles. We theoretically prove that the proposed algorithm can achieve theglobal optimum in stylized networks, while the numerical experiments based onboth the toy network and realistic dataset demonstrate that MMA is capable ofachieving superior systematic performance compared to batch matching andreinforcement-learning based methods. Moreover, its modular and lightweightmodeling structure further enables it to achieve a high level of robustnessagainst demand variation while maintaining a relatively low computational cost.

Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach