Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy

Abstract

Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers’ service level experience and increase the safety risk at congested platforms. This paper focuses on designing an online passenger flow control policy to manage passenger flow in each origin-destination (OD) pair so that the total passenger waiting time during the research horizon can be minimized. Suppose that the OD demand information reveals sequentially over time, we formulate the online passenger flow control problem as stochastic dynamic programming (DP). An efficient online adaptive policy is designed to guide the real-time flow control decisions at each stage. To evaluate the performance of our approach, we exploit the realistic transit data from the Beijing metro system to carry out a series of numerical experiments. The computational results show that our approach can significantly reduce the expected total passenger waiting time as well as alleviate metro station congestion compared with the first-come-first-serve (FCFS) policy. The benefits of our approach are obtained by exploiting the reusable nature of train capacity to transport more passengers during rush hours.