An integrated framework of routing and rebalancing for RoboTaxi systems

The RoboTaxi service is considered a groundbreaking mode of transportation, offering autonomous ride services to passengers. Once a travel request is placed, the passenger is picked up promptly and transported directly to their destination. Following a trip, the RoboTaxi can immediately serve another customer, remain idle, or relocate within the city to anticipate future demand. The operation of RoboTaxi systems involves two key processes: routing and rebalancing. Routing determines which vehicle will serve a passenger and in what order. Rebalancing involves moving idle vehicles to strategic locations to improve passenger satisfaction for future requests. This relies on short-term demand prediction to ensure efficient resource allocation. While existing research has primarily considered these components independently, this study integrates them into a comprehensive framework designed to improve operator profitability and passenger satisfaction. In addition, different prediction methods are adopted to examine the impact of prediction accuracy on optimization results. The results indicate that the proposed framework achieves an approximate 12 % increase in operator profits and a significant improvement in the acceptance ratio.