Research On Optimization Of Metro Train Operation Timetable Based On Cluster Analysis Of Passenger Flow Temporal And Spatial Characteristics

During the 13 th Five-Year Plan period,my country’s urban rail transit developed rapidly.The investment in rail transit construction was as high as RMB 2627.87 billion,and the average annual construction investment was 525.57 billion RMB,which doubled during the 12 th Five-Year Plan period.By the end of 2020,my country’s metro system accounted for the proportion of cities The rail transit operating system is about80%.The subway train operation timetable is the core element of the subway operation organization.The development of a scientific and reasonable operation timetable is of great significance for improving passenger carrying efficiency,passenger satisfaction,and improving enterprise operation service level and reducing train operation and labor costs.In this paper,based on the actual data of the passenger flow on weekdays,non-working days and the passenger flow of each station in the east section of Beijing-Hong Kong Metro Line 14,the k-means clustering algorithm is used to cluster and analyze the time and space distribution of passenger flow.Research the methods and theories of commonly used train operation diagrams,comprehensively consider the actual situation of line passenger flow,communication,signal equipment,etc.,construct a subway train operation timetable optimization research model based on dynamic passenger flow analysis,and select particle swarm algorithm based on the characteristics of the analysis model.The algorithm parameters are adaptively improved to increase the efficiency and accuracy of the solution.Finally,the results of temporal and spatial passenger flow clustering are substituted into the optimization model,and the optimization results are output through Matlab mathematical planning software: the total cost of subway train operation schedule on weekdays is reduced by 3.4%;the total cost of subway train operation schedule on non-working days is reduced by 11.7%.The main work of this paper is as follows:(1)According to the formation of subway passenger flow,the influencing factors of subway passenger flow,and the distribution characteristics of subway passenger flow,conduct a comprehensive analysis of Beijing subway passenger flow,study the time and space characteristics of subway passenger flow,and provide follow-up passenger flow cluster analysis and train operation time Table optimization and adjustment provide theoretical support.(2)Carry out specific analysis with the goal of minimizing the total cost of passenger travel and operating enterprise operating costs,construct a subway train operation schedule optimization model,and select an adaptive particle swarm algorithm according to the complexity of the model solution,and set particle swarms according to the actual situation The basic parameters of the algorithm.(3)Apply the results of time and space cluster analysis of subway passenger flow combined with the optimization model of subway train operation timetable,input passenger flow data,train data and timetable data of the east section of Beijing-Hong Kong Metro Line 14,and use adaptive particle swarm algorithm to solve it,Output the optimal working day train operation schedule and the optimal non-working day train operation schedule.The analysis shows that the operation schedule optimization model in this paper has a good effect on reducing the cost of both passengers and enterprises.The optimization model can be verified by examples.Reducing costs has certain practical significance and reference value.