Urban Rail Transit Train Timetable Optimization Based On Passenger Flow Analysis Of Spatio-temporal Network

With the rapid development of China’s society and economy,the number of private cars has increased dramatically,making urban roads congested,causing inconvenience to people and serious environmental pollution.Urban rail transit has the advantages of large capacity,fast speed,safety,and environmental protection.Guiding more residents to use urban rail transit is the most effective way to solve traffic congestion.However,as the number of urban rail transit users increases,the quality of train services and passenger comfort decreases.Therefore,analyzing the passenger flow of urban rail transit and knowing the situation of the passenger on the whole network is very important for the adjustment of urban rail transit operations.The rationality of train schedules has a direct impact on the transportation capacity and service capacity of urban rail transit.The timetable needs to be adjusted and optimized according to the passenger flow situation to improve the operating efficiency of urban rail transit and increase the train service capacity.This study takes the network operation of urban rail transit as the background,analyzes the passenger flow of urban rail transit based on AFCS(Automatic Fare Collection System)passenger flow historical transaction data,and studies the construction and solution of train schedule optimization models.This study first summarizes the research status of domestic and international urban rail transit passenger path allocation methods,passenger flow analysis and train schedule optimization;then it introduces the AFCS passenger flow data collection and preprocessing methods,and the shortest path method is combined with the actual travel time of passengers to calculate the specific travel path of each passenger;next analyze the passenger flow of the urban rail transit,divide the passengers into direct passengers and transfer passengers,and use the backward push time method and the waiting time combination probability method to obtain the passenger boarding situation,the specific waiting time and the average standing area of passengers on the train,this data could evaluate the service quality of the train,based on the Chengdu rail transit data,an example analysis is performed to obtain the early peak operating conditions of Chengdu rail transit network and passengers waiting situation on the most congested lines and stations;finally,taking the departure interval of the train during the morning rush hour as the decision variable,with the goal of minimizing the waiting time of passengers,an urban rail transit timetable optimization model is established,the model is solved by genetic algorithm,the model is verified by using Chengdu rail transit line as an example,and the result of optimizing its early peak train schedule shows that the optimized schedule can effectively reduce the waiting time of passengers.