{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-agent-deep-reinforcement-learning-based-3","title":"Multi-agent deep reinforcement learning based real-time planning approach for responsive customized bus routes","arxiv_id":null,"date":"2023-12-14","proceeding":"journal 2023 12","authors":["Binglin Wu","Xingquan Zuo","Gang Chen","Guanqun Ai","Xing Wan"],"abstract":"Customized bus can meet many passengers’ personalized travel demand in a public transportation system by\r\nproviding an innovative shared travel service. Customized bus offers multiple bus routes that jointly form\r\na route network to serve its passengers. It must frequently adjust the station sequences of each bus route\r\nin response to trip cancellations and new trip bookings during its operation. Different from relevant research\r\nworks in the literature, this paper proposes a multi-agent deep reinforcement learning based real-time planning\r\napproach for tackling the multiple customized bus routes planning problem. We model the problem as a multi-\r\nagent Markov decision process for the first time in literature where a separate agent is assigned to each route\r\nto plan its station sequence. We then develop a new multi-agent system. Each agent in the system is powered\r\nby an encoder–decoder neural network that consolidates the station sequence decision policy for each bus\r\nroute. We employ a policy gradient-based reinforcement learning algorithm to train the network parameters\r\nof the multi-agent system so as to maximize the number of passengers served while ensuring the customized\r\nbus service quality and minimizing the operating cost of all customized bus routes. On three (six) problem\r\ninstances in offline (online) scenarios, the trained multi-agent system can significantly outperform several\r\nexisting algorithms in terms of the total cost, adaptiveness and computation time.","url_abs":"https://doi.org/10.1016/j.cie.2023.109840","url_pdf":"https://doi.org/10.1016/j.cie.2023.109840","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-agent-deep-reinforcement-learning-based-3","repo_url":"https://github.com/BUPTAIOC/Transportation/tree/main/Multi-Agent%20Deep%20Reinforcement%20Learning%20based%20Real-time%20Planning%20Approach%20for%20Responsive%20Customized%20Bus%20Routes","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":null,"method_name":null},{"method_slug":null,"method_name":"Travel"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}