Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model

Abstract

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments.