Online Relocating and Matching of Ride-Hailing Services: A Model-Based
  Modular Approach
Chang Gao Xi Lin Fang He Xindi Tang
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.