Study On Cooperative Optimization Train Timetabling And Rolling Stock Scheduling Model And Algorithm For Urban Rail Transit

With the acceleration of the process of China's urbanization,the contradiction between transportation supply and demand is becoming increasingly prominent.Urban rail transit has the characteristics of safe,convenient,fast and low pollution.These characteristics make urban rail transit become the one of the important tools for solving the problems of urban resident's travel.And the urban rail transit has been rapidly constructed and developed.However,the process of making the train timetable is mainly based on the experience and judgment of the operation organizer,this method lacks the precise and quantified calculation process,which cannot effectively match the passenger flow.Based on a careful analysis of the existing research results at home and abroad,considering the micro-dynamic passenger demand and the rolling stocks scheduling,a coordinated optimization method for integrated train timetable and rolling stock schedule is studied.The main contents are as follows:(1)The dissertation discusses the characteristics of the urban rail transit train timetable,analyzes the evaluation indicators of the train timetable from two aspects of passengers and operating companies.The imbalance of the spatial and temporal distribution of the dynamic passenger flow and the characteristics of the traffic flow trajectory and the operation process of the rolling stocks are focused on.Based on the full understanding of the characteristics of passenger flow,traffic flow and rolling stock schedule,this paper analyzes the interrelationship among the three,and establishes the optimization method of urban rail transit organization based on time-varying dynamic passenger flow.(2)Based on the analysis of micro passenger arrival intensity and the dynamic passengers getting on and off at the station,the train timetable optimization model under the time-dependent passenger flow conditions,which couple passenger arrival intensity,is established.The model uses the train departure time as a decision variable,and considers train running time,train full load rate,passenger demand as the relevant constraints,aiming at reducing passenger waiting costs and congestion costs.Aiming at the characteristics of the model,the special coding form of PSO-GA algorithm is designed to solve the problem,and the MATLAB software is used to solve the program.The case study validates the effectiveness of the model.(3)Considering the process of the rolling stocks,the mathematical model for the optimization of the train timetable and rolling stock schedule under time-varying conditions is established.Aiming at the unbalanced passenger flow on uplink and downlink during peak periods,the empty train scheduling model is designed to speed up rolling stock operation process.On the basis of the train timetable utilization model under time-varying conditions,the model adds constraints such as rolling stock turnaround time constraints,train return line capacity constraints,and the number of empty train departures constraints,etc.The train timetable considering the rolling stocks cost is provided,which couples time-varying passenger flow intensity.And the algorithm is programmed.Through the case,the rolling stock schedule dominant train timetable and collaborative optimization train timetable are obtained.The costs of two train timetable are calculated and compared separately with the cost of the balanced train timetable to verify the validity of the model.Finally,through the analysis of sensitivity,it provides guidance for operating companies to consider the different train timetable according to the passenger flow conditions and operational conditions.