Integrating Regional Traffic Models for Ride-Hailing Systems in a Multi-Layer Repositioning Framework

This paper introduces a multi-layer control strategy for efficiently repositioning empty ride-hailing vehicles. The proposed framework consists of three layers: an upper-layer employing an aggregated model based on the Macroscopic Fundamental Diagram (MFD) and model predictive control (MPC) to determine optimal vehicle repositioning flows between regions; a middle-layer converting macroscopic decisions into dispatching commands for individual vehicles, and a lower-layer utilizing a coverage control algorithm for local demand-aligned positioning. The upper-layer contributes to the proposed framework by providing a global view and predictive capabilities. The middle-layer contributes by ensuring and optimal assignment of repositioning vehicles, considering the decision from the upper-and lower-layers. Finally, the lower-layer contributes by providing the precision required for microscopic vehicle guidance. Experimental validation using an agent-based simulator on a real network in Shenzhen confirms the effectiveness and efficiency of the framework in improving empty vehicle repositioning strategies for ride-hailing services in terms of average passenger waiting time and abandonment rates.