A Multiline Customized Bus Planning Method Based on Reinforcement Learning and Spatiotemporal Clustering Algorithm

The demand-responsive customized bus has been operated in real life, which is a crucial way to improve the service quality and efficiency of the urban public transportation system. Reasonable station and line planning can enhance customized bus competitiveness in residents' travel mode. Most previous studies on optimizing customized bus lines rely on historical passenger volume and travel time to generate static schemes, but the actual operation process of customized bus is often in uncertain circumstances, such as road congestion. The static strategy will occur deviations in this situation. This study proposes a novel planning method to address the above issue. First, a spatiotemporal clustering algorithm is proposed to generate joint stations based on the passenger travel demand. Second, the method models the multiline customized bus optimization problem as a Markov decision process and uses a multiagent deep reinforcement learning algorithm to ensure effective training and response to incomplete information scenarios. Finally, the rationality of the proposed planning method is verified in a case study of customized bus area in Chongqing, China. Compared with the latest heuristic optimization algorithm, our method can effectively reduce the operating and passenger costs in complex environments.