北京交通大学学报2025，Vol.49Issue(4)：19-28,10.DOI:10.11860/j.issn.1673-0291.20240150
基于多智能体强化学习的地铁接驳高铁客流疏散优化研究
Study on optimization of metro-to-high-speed rail passenger flow dispersion based on multi-agent reinforcement learning
摘要
Abstract
To address challenges such as passenger crowding,excessive waiting times,and inefficient use of transportation resources in metro-to-high-speed rail transfer scenarios,this study proposes an optimization method for metro-to-high-speed rail passenger flow dispersion based on Multi-Agent Re-inforcement Learning(MARL).The method dynamically adjusts metro timetables to enhance passen-ger dispersion efficiency,alleviate crowding,and improve the utilization of transportation resources.First,the metro-to-high-speed rail passenger flow dispersion optimization problem is formulated as a Markov game by integrating the spatiotemporal information of metro operations and the spatiotempo-ral characteristics of passenger transfers,with general state features,action space,and a reward func-tion specifically designed.Second,a multi-agent decision-making model is then developed using the Actor-Critic(AC)framework,and an asynchronous action coordination mechanism is introduced within a centralized training and distributed execution architecture to enhance training efficiency.Fi-nally,an optimization study is conducted using the Tianjin West railway station as a case study.Re-sults indicate that the proposed method significantly reduces passenger waiting times and improves metro operational efficiency.The average passenger waiting time decreases by 26.80%,while the av-erage metro operational efficiency increases by 14.11%.关键词
多智能体强化学习/地铁接驳/客流疏散/异步动作协同机制Key words
multi-agent reinforcement learning/metro connection/passenger flow dispersion/asyn-chronous action coordination mechanism分类
交通工程引用本文复制引用
孙峣,柯水平,贾宁,辛秀颖..基于多智能体强化学习的地铁接驳高铁客流疏散优化研究[J].北京交通大学学报,2025,49(4):19-28,10.基金项目
国家自然科学基金(52372313) （52372313）
天津市人社局"131"创新团队项目(2019-44)National Natural Science Foundation of China(52372313) （2019-44）
The"131"Innovation Team Project of Tianjin Municipal Bureau of Human Resources and Social Security(2019-44) （2019-44）
