Preference-Aware Vehicle Repositioning Recommendation for MoD Systems: A Coulomb Force Directed Perspective

Abstract

Vehicle repositioning is widely used in Mobility on-Demand (MoD) systems to address supply-demand imbalances and improve order completion rates. Existing methods typically offer repositioning recommendations focused on enhancing vehicle coordination toward supply-demand re-balance. However, these methods often overlook the possibility that drivers may not follow these recommendations due to their personal preferences, leading to recommendation-decision inconsistency and further disrupting the supply-demand balance. To address this issue, we propose a preference-aware vehicle repositioning recommendation strategy for MoD systems, named FREE, which is based on a Coulomb Force directed approach. The core idea is to strike a balance between vehicle coordination and consistency between recommendations and driver decisions. First, we introduce a Coulomb force-based representation (CFR) to model coordination among vehicles. In this model, the interactions between vehicles and orders are represented as forces that drive the repositioning of vehicles. Next, we develop a driver preference learning model that accurately captures drivers’ preferences using triplet and consistency loss. We then integrate these preferences with the CFR into a multi-agent deep reinforcement learning (MADRL) based repositioning algorithm to generate optimal recommendations. Finally, we validate the effectiveness of FREE through simulations using real-world data, demonstrating its superiority over existing benchmarks.