DRiVe: An Enhanced Coupling Design of Demand Prediction and Repositioning for Shared Autonomous Vehicle Systems

In shared autonomous vehicle systems (S-AVS), efficient and adaptive vehicle repositioning plays a crucial role in meeting time-varying traffic demand, typically achieved by leveraging user demand prediction. However, most existing studies treat traffic demand prediction and shared autonomous vehicle (SAV) scheduling as separate tasks, ignoring the tight interaction between the two components, such as the potential impact of scheduling results on demand prediction. Such a design lacks a deep integration for both, potentially leading to inaccurate predictions and less efficient repositioning performance. To address this issue, we propose DRiVe, an enhanced coupling design for Demand prediction and Repositioning for shared autonomous Vehicle system. Two corresponding coupling strategies are designed, differentiated by their respective coupling locations. Specifically, we consider electric SAVs and employ model predictive control (MPC) to develop the repositioning strategy, aiming to minimize the operator's repositioning costs and passenger dissatisfaction. An online traffic demand prediction mechanism is introduced to transform scheduling actions into additional traffic demand. This additional demand is then incorporated into traditional traffic demand prediction to enhance the accuracy of the final demand prediction. The numerical results indicate that the proposed DRiVe method outperforms existing approaches in reducing passenger waiting times and repositioning distances.