Optimizing total passenger waiting time in an urban rail network: A passenger flow guidance strategy based on a multi-agent simulation approach

With the sharp increase of passenger travel demands in an urban rail transit (URT) network, more and more stations suffer an over-saturated and congested situation during peak hours, which often leads to colossal passenger accumulation in platforms, especially in transfer platforms. Under this state, the passenger flow guidance is a useful method to release the passengers' pressure and balance the passenger accumulation imbalance. In order to calculate the passenger flow traveling data and the passenger flow guidance (PFG) time, a multi-agent simulation model is firstly established. And then, a two-phase integrated passenger flow assignment based on the backtracking algorithm (BA) is proposed for generating guidance information to minimize total passenger waiting time and release passenger congestion. Besides, a passenger compliance degree to the guidance information is defined to stimulate the passengers' response to the guidance information in the real world. Finally, a real-world example of the Chongqing subway network with five metro lines and 95 stations is implemented to verify the performance and effectiveness of the proposed method. The total passenger waiting time is decreased by 5.62% under the passenger flow guidance approach.