A Collaborative Optimization Method Of Passenger Flow Control Strategy And Train Timetable For Metro Systems

In recent years,with the acceleration of urbanization,the metro has become one of the most popular ways of transportation in the modern metropolis.The metro has the advantages of high passenger capacity,high punctuality,high efficiency,low energy consumption and low pollution.It is considered to be a very effective solution to alleviate traffic congestion in megacities.Efficient and accurate optimization methods play a crucial role in alleviating passenger flow congestion during peak hours.Using the optimized control strategy,the metro station passengers can be guided and controlled accurately and timely.It improves passenger efficiency by allowing passengers to board arriving trains more quickly in these methods.The rapid development of optimization theory provides good technical support for solving the optimization problem of passenger flow congestion.Exploring efficient optimization methods of metro operation is of great significance to alleviate passenger congestion during peak hours and improve the operation service level of the metro system.Aiming to the problem of over-saturation of passenger flow demand in peak hours,based on the analysis of passenger travel Origin-Destination(OD)demand,this paper proposes a collaborative optimization method of metro network flow control and timetable.Based on ensuring operation safety,the method can improve operational efficiency and reduce passengers’ travel delays.The main work and achievements of this paper are as follows:(1)A collaborative optimization model of flow control and timetable is constructed.This paper analyzes passengers’ travel OD demand.The coupling relationship between passenger flow and train flow is considered.Floyd’s shortest path method is used to process the actual passenger flow data of automatic fare collection(AFC)card swiping and analyze the passenger travel path.Based on the passenger flow distribution,the passenger flow and train flow are coupled and optimized.The collaborative optimization mixed integer nonlinear programming(MINLP)model of flow control and timetable is established.The collaborative optimization model is designed to minimize the average waiting time of passengers and reduce travel delays and the average travel time of passengers.(2)The improved differential evolution algorithm and the elite reservation genetic algorithm for the collaborative optimization model of flow control and timetable are designed.Based on the research of the control parameters,difference strategy and selection strategy of the basic differential evolution(DE)algorithm,this paper designs the improved DE algorithm of the collaborative optimization model.The genetic algorithm(GA)of the elite reservation under the collaborative optimization is designed to compare and solve the model respectively.Compared with the classical GA,the DE algorithm has the advantages of simple structure,easy operation,rapid convergence and strong robustness.It can accurately and quickly find the optimal strategy of collaborative optimization of flow control and timetable of the metro system.(3)An empirical analysis is made on some lines of the Beijing metro system.In this paper,the complex relationship between transfer stations on the road network is considered.An empirical analysis of the established collaborative optimization model is carried out by using python programming software.The high-quality solution of collaborative optimization of metro flow control and timetable is found.The optimization results of the improved DE algorithm under collaborative optimization and the elite reservation GA algorithm are compared.The optimization results of the metro system’s four operating conditions are compared under the solution of the improved DE algorithm.The experimental results show that the improved DE algorithm can get better results for solving the collaborative optimization model.Compared with no optimization,the average waiting time of passengers under collaborative optimization is reduced by 26%.Compared with the metro operation condition of only flow control and only optimizing timetable,the results obtained by the collaborative optimization method of flow control strategy and timetable are the best.There are 38 pictures,11 tables and 70 references.