  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.
