Rapid urbanization and population growth have led to a continuous increase in passenger flow in urban rail transit,which presents significant challenges to the safety,comfort,and stability of rail transit operations.To solve the problem of excessive load rate of urban rail transit during peak hours,we propose a cooperative passenger flow control method for urban rail transit based on deep reinforcement learning.This method uses the full load rate between intervals as its state,a flow restriction strategy as its action,and the passenger flow experience as its reward.It generates an optimal flow restriction scheme through multi-round reinforcement learning.We validated the effectiveness of this method by constructing simulation experiments using data from the Beijing subway network.The simulation results show that the cooperative passenger flow control method can effectively reduce passenger flow in a section,relieve congestion during peak hours,and improve passenger travel comfort.
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