Study On Timetable Optimization Of Urban Rail Train Based On Dynamic Passenger Flow

With the development of the national economy and the continuous innovation of science and technology,many new modes of transportation have emerged.As a distinctive mode of transportation,urban rail transit plays an important role in the urban transportation systems,which has become an effective mean to mitigate urban traffic congestion due to its high capacity,high speed,convenient,punctuality,high efficiency and energy saving.In the urban rail operation,the urban train timetable is one of crucial factors to influence the quality of the service,which not only stipulates the arrival time and departure time of each train,but also provides a reliable service for passengers.In addition,the passenger flow in urban rail transit is dynamic.That is,the passenger flow is obvious difference under different time and space.Therefore,this thesis provides two optimizations model of train timetabling problem in the urban rail transit based on the dynamic passenger flow,and coordinating passenger demand and transportation supply,which is of great significance for improving passenger satisfaction and operational efficiency.In this thesis,the time and space distribution characteristics of urban rail transit passenger flow are analyzed.Then the time-space network method is used to transform the timetable optimization problem into the train routing optimization problem,which is also used to formulate into the periodic(same departure time interval)timetable and the aperiodic(uncertain departure time interval)timetable optimization models.Finally,the real-world data collected by the automatic ticketing system(AFC)is used to verify the validity of the model on the Beijing metro Yizhuang line.Specifically,the main contributions are as follows:(1)This thesis analyzes the characteristics of urban rail transit passenger flow distribution,including the distribution characteristics in time and space.Then the record of passenger information in the urban rail transit and the extraction method of passenger flow data are discussed respectively,which provide the data source for the application of the optimization model.(2)Based on the characteristics of urban rail transit dynamic passenger flow,the real-world physical subway line and stations are transformed into virtual networks by using the space-time network method,which transforms the complex train timetable optimization problem into a relatively simple train routing optimization problem.Then two timetable optimization models whose objective functions are minimum passenger waiting time are built by considering the real-world constraints in the train operation in the virtual space-time network.Model 1(balanced passenger flow)optimizes the route of each train in the space-time network to obtain the optimal timetable under the same train departure interval;Model 2(unbalanced passenger flow)optimizes the route of each train in the space-time network to obtain the optimal timetable under the uncertain train departure interval.Finally,two examples are implemented to verify the validity of the model.(3)Based on the real-world data,some examples are implemented in different time periods(morming-peak,off-peak,and evening-peak)and different scenes(working days,weekends)of the Beijing metro Yizhuang line,and solved by CPLEX 12.6.Finally,the optimized results are compared and analyzed with the real-world timetable,which prove that the rationality and effectiveness of proposed model in the thesis.