A Coupling Approach to Demand Prediction and Repositioning in SAV Systems

In Shared Autonomous Vehicle (SAV) systems, real-time vehicle repositioning plays a crucial role in meeting time-varying traffic demand, which is normally designed by taking advantage of user demand prediction. Nonetheless, most existing studies only predict traffic demand and schedule SAVs separately, ignoring the tight interaction between the two components, e.g. the potential impact of repositioning results on demand prediction. Such a design lacks a deeply integrated design for both and may lead to inaccurate demand prediction and impaired repositioning performance. To tackle this challenge, we propose DRiVe, a coupling approach to Demand prediction and Repositioning for shared autonomous Vehicle system. Specifically, we consider electric SAVs and adopt model predictive control (MPC) to develop the repositioning strategy with the goal of minimizing the operator's repositioning costs and passenger dissatisfaction. An online prediction is then introduced which not only implements the traditional demand prediction but also integrates the additional traffic demand generated by repositioning action. The numerical results demonstrate that the proposed DRiVe method achieves better performance in reducing passenger waiting time and idle distance compared to the state-of-the-art repositioning methods.