Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, Jinhua Zhao
TL;DR
This paper tackles fairness in ride-hailing vehicle rebalancing by addressing both algorithmic fairness in demand prediction and rider fairness in downstream service delivery. It introduces a socio-aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for demand forecasting and a fairness-weighted Matching-Integrated Vehicle Rebalancing (MIVR) for rebalancing, with region-specific weights derived via CP decomposition of an enriched adjacency. Empirical results on NYC data show that fairness-aware predictions yield substantial gains in equity (lower MVPE and GEI) while maintaining or improving accuracy, and that fairer upstream predictions translate into more equitable downstream service, including reduced wait-time variance. The work highlights practical policy implications and the need for driver incentive mechanisms to realize a true win–win in efficiency and equity for ride-hailing systems.
Abstract
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.