Joint Rebalancing and Vehicle-to-Grid Coordination for Autonomous Vehicle Public Transportation System

An Autonomous Vehicle (AV) is believed to be the next generation transport that can enhance safety and efficiency for smart mobility. In an AV-based public transportation system, the full autonomy of AVs enables high-efficiency transport services and the potential ride-sharing feature of AV system enhances the utilization. The system manages a fleet of AVs, determines their assignments to transport requests, sends instructions concerning the optimized plans, and recommends the parking locations for the unoccupied AVs. The parking location of an empty AV is crucial in the sense that rebalancing AVs to areas with high potential service demand can curtail the unnecessary waiting time for passengers. As AVs are generally electric, proper parking locations can also facilitate vehicle-to-grid (V2G) support. In this paper, we propose a joint rebalancing and V2G coordination strategy for AV-based public transportation system. We formulate the problem as an integer linear program and propose a heuristic based on Genetic Algorithm and Model Predictive Control to solve the problem in low time complexity. Extensive experiments are performed with the real taxi service data from New York City. The formulated integer linear program is solved dynamically where each problem instance contains 3 to 5 AVs and 3 to 8 requests in 30s time interval. Compared with the transport system without rebalancing, the results show that the coordination strategy is efficient and effective in reducing unnecessary waiting time for passengers while satisfying V2G support. Compared to computational time with the standard solver, the proposed heuristic dramatically reduces the computational time.