Study on optimization of metro-to-high-speed rail passenger flow dispersion based on multi-agent reinforcement learning
SUN Yao
KE Shuiping
JIA Ning
XIN Xiuying
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%.
Keywords：multi-agent reinforcement learningmetro connectionpassenger flow dispersionasyn-chronous action coordination mechanism
Publication Date：2025-08-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 19-28 )