A Method for Robotaxi Dispatch with Recommendation of Boarding Time and Pick-Up/Drop-Off Points

With the fast progress in autonomous driving and communication technologies, robotaxis emerge as a novel mode of transport. Optimization of robotaxi dispatch with ride-sharing can enrich the travel choices of residents and improve the network capacity of transportation systems. This work proposes a multi-objective optimization model for robotaxi dispatch. Unlike previous approaches, this is the first attempt to adjust simultaneously unreasonable boarding time (BT) and pick-up/drop-off (UO) points for passengers during the dispatch. It encourages passengers to walk to the recommended UO points or to be picked up slightly earlier or later, which aims to maximize the profit per kilometer of robotaxis and to minimize the total travel expense of passengers. Consequently, a nondominated sorting genetic algorithm with mass center (NSGA-MC) is proposed to address the model. Experimental results show that the proposed algorithm outperforms its peers from multiple metrics, which highlight the advantage in advancing intelligent public transportation systems.