Research On Passenger Flow Simulation And Control Of Urban Rail Transit Station Based On System Dynamics

The rail transit system is an important part of urban public transport.Facing the sharp increase in residents’ travel demand,the limited transport capacity and efficiency of rail transit has been difficult to meet.Especially in the morning and evening peak hours,passenger flow congestion at rail transit stations has become a common problem.In order to ensure the quality of station service and passenger travel safety,passenger flow control during peak hours is already the normal state of some stations.This paper focuses on the problem of passenger flow congestion in rail transit stations,and uses the method of system dynamics to model and simulate the passenger flow deduction rules in rail transit stations.On this basis,a multi-mode passenger flow control model for rail transit stations is established,which is solved by adaptive multi-group particle swarm optimization algorithm based on inter generational crossover.Select a typical station as a case to verify the feasibility of the model and form an optimal control strategy.The main contents of this paper are as follows:(1)Analysis of passenger flow characteristics of rail transit stations,observation and analysis of passengers’ movement characteristics and running streamlines in the station,then using the station as a complex large-scale integrated system,dividing the internal facilities and equipment by nodes,analyzing the internal relationship of various elements and the logical framework structure.After considering the system causality and the passenger flow path,the station passenger flow conversion model is established based on the system dynamics,and the flow of the passenger flow along the different passenger flow lines and between the facilities in the station is simulated.(2)Based on the simulation model of rail transit station,a multi-mode passenger flow control model is established.Considering the internal conditions of the station,determining the passenger flow control objectives and evaluation indicators,and the corresponding constraints,establishing a passenger flow control model for a single station.And the adaptive multi-group particle swarm optimization algorithm based on inter generational crossover is used to solve the model.Based on the simulation model of the rail transit station,establishing a passenger flow control model.Considering the internal conditions of the station,determining the passenger flow control objectives and decision variables,and the corresponding constraints,establish a passenger flow control model for a single station.And the adaptive multi-group particle swarm optimization algorithm based on inter generational crossover is used to solve the model.(3)Taking the Beijing Metro Guomao Station as a typical station,the actual survey data is fitted to the model parameters,developed using the Java language,simulating the passenger flow of the station during the peak period,and obtaining key statistical indicators such as passenger flow,passenger flow density and passenger circulation time.The simulation results are compared with the actual survey data to verify the validity and feasibility of the proposed passenger flow simulation model.Then,based on the simulation model,the optimal solution of the passenger flow control model is obtained,and the passenger flow control strategy is formed according to the solution result.Compared with the station normal control strategy,the proposed passenger flow control scheme is better.