Research On Collaborative Optimization Of Dynamic Fare And Timetable In Urban Rail Transit Peak Period

The contradiction between urban rail transit demand and traffic supply has become increasingly prominent.In some large cities,the distribution of subway daily passenger flow is not balanced,and there are obvious peak and trough periods.Especially in the peak period,the number of subway platforms is too large,and there are potential safety hazards.The high degree of congestion and poor experience of passengers greatly reduce the enthusiasm of urban residents to choose subway as a way of travel.Under the condition of limited time and space,the peak passenger flow congestion of urban rail transit is an urgent problem to be solved.Under the premise of keeping the operator to meet the revenue,it is necessary to put forward the strategy of peak passenger flow control of urban rail transit,flexibly grasp the existing optimization methods in China,alleviate the pressure of urban passenger flow,and provide theoretical and technical support for improving train utilization efficiency and passenger service level.Therefore,from the perspective of traffic supply management and traffic demand management,it is of great significance to study the train timetable and dynamic pricing strategy under the background of peak passenger flow.This paper studies the collaborative optimization of dynamic fares and timetables during peak hours of urban rail transit.The specific research contents are as follows :(1)Analyzing the characteristics of peak passenger flow and time-sharing pricing theory.Firstly,the characteristics of passenger flow composition during peak hours are excavated.Secondly,the dynamic pricing method is analyzed from the perspective of congestion pricing and time-sharing pricing,and the relationship between fare changes and passenger demand is explored.(2)An optimization model of urban rail transit timetable for minimizing the number of delayed passengers is established.The coupling relationship between trains and passengers is analyzed.The optimization model of urban rail transit timetable considering dynamic passenger flow is constructed and solved by simulated annealing algorithm with the minimum sum of the total number of people in the station.Based on the actual operation data of an urban rail transit line,the results show that by adjusting the departure interval of the train,the number of passenger delays is significantly reduced,and a variety of scenarios are constructed to explain the impact of the number of vehicles on the relevant indicators.(3)A pricing model of urban rail transit based on optional path set is established.Firstly,the concept of passenger acceptable offset train times is introduced,and a three-dimensional network based on time-space-fare is constructed.Taking the maximum fare revenue as the optimization objective function,considering revenue constraints,passenger flow constraints and train service capacity constraints,an urban rail transit split pricing model based on space-time state network is constructed.Then,the model is solved by the simulated annealing algorithm nested with the flow assignment method based on the selection probability.Finally,a case is used to verify the correctness of the model and algorithm,and the calculation results are analyzed and discussed.(4)A pricing model of urban rail transit considering train arrival and departure time is established.Similarly,taking the maximum fare revenue as the optimization objective function,considering revenue constraints,passenger flow constraints,train service capacity constraints,train arrival and departure time constraints,and train departure interval constraints,a collaborative optimization model of urban rail transit dynamic pricing and timetable based on spatio-temporal state network is constructed.The algorithm and examples of the same(3)are used to analyze and discuss the calculation results and compare them with the results of(3).The collaborative optimization strategy of train timetable and train pricing designed in this paper takes into account the ’system optimization’ of reducing operating costs and the’user optimization’ of meeting passenger travel demand,and achieves the effect of passenger flow ’peak shaving and valley filling’ in the peak hours of urban rail transit.