Research On Urban Rail Transit Train Timetable Optimization Models And Algorithms Based On Passenger Waiting Time

The new science and technology revolution has promoted the development of urban rail transit and put forward new requirements for matching capacity and passenger demand accurately.During the urban railway operation,the train timetable is a key component to provide travel reference for urban rail passengers and work arrangement for operators.The influences of passenger flow uncertainty in practice are considered to adjust the train timetable to avoid the failure of the optimal timetable and offer a reliable support system for operators.Based on uncertain passenger flow and single-loop urban rail transit,factors such as passenger flow characteristics,the number of train services,train number,train capacity,and train operation safety are considered in the models from the operator’s point of view.After proposing a train timetable optimization model under certain conditions,an all-day train timetable optimization model with changing number of services train under uncertain conditions is proposed to minimize the generalized cost.The generalized cost includes waiting time cost and train service cost.Besides,a genetic harmony algorithm is designed to solve the models,which can speed up the iteration and obtain pleased nearoptimal solutions.Specifically,the details of this paper are:(1)From the perspective of matching capacity and demand,an optimal train timetable model under certain conditions is established.The calculation method of passenger waiting time including multiple waits under capacity limitation is given by analyzing the relationship between the passenger arrival and train departure time.Then the number of train services is determined based on the passenger demand.The train arrival and departure times at each station are optimized under the constraints of train operation safety.Finally,A genetic harmony algorithm is designed to solve the model.The comparison between the genetic harmony algorithm and the typical genetic algorithm shows that the convergence speed and solving process of the genetic harmony algorithm is better than the typical algorithm.(2)Different scenarios are set considering passenger arrival time at the station may be affected by various factors in the actual situation.Meanwhile,a binary variable is introduced to control the number of train services.A train timetable optimization model under uncertainty is established with changing number of train services.The arrival and departure time of the train at each station and the number of train services are optimized by minimizing the converted cost of expected waiting time under different scenarios and the converted cost of the number of train services.Finally,A genetic harmony algorithm is designed,and a case study of the Beijing Yizhuang Line verifies the correction of the model and the solving advantages of the algorithm.The model and algorithm give a good application prospect for the operation of urban rail transit.