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

TL;DR

This paper introduces a modular, model-based approach for real-time order matching and vehicle relocation in ride-hailing, demonstrating superior performance and scalability through theoretical proofs and numerical experiments.

Contribution

It presents a novel two-layer modular framework with a polynomial-time algorithm that guarantees global optimality in stylized networks and scales efficiently to large systems.

Findings

Achieves global optimality in stylized network models.

Outperforms batch matching and reinforcement learning methods in experiments.

Maintains robustness and low computational cost under demand variability.

Abstract

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.