Research On Coordinated Optimization Of Passenger Flow Control In Urban Rail Transit Under Uncertain Conditions

With the rapid development of urban rail transit,operation management has been fully networked.It brings convenience to passengers,but also accompanied by some problems in the operation.For example,there is a serious imbalance between passenger demand and traffic supply capacity in some time periods,resulting in more passengers staying at the station platform and taking more time to wait on the platform.The changes in passenger density also bring uncertainty to the station's operation safety,the turnover volume overall passenger,and the passenger travel time.How to use effective passenger control decisions to coordinate passenger flow,to balance traffic demand and supply between urban rail transit network,which is the focus of this thesis.In order to find out the optimal scheme of passenger flow control in urban rail transit,the implementation plan is made.In the first stage,the passenger flow is divided into different scenarios,the uncertainty of travel time is analyzed,the uncertain variables are robust optimized,and the approximate model is transformed into a deterministic model.In the second stage,the network of urban rail traffic congestion transmission and the purpose of the passenger flow control coordination are analyzed.In the third stage,an effective coordination optimization model of passenger flow control is established.By introducing a certain time step to control the control time,the receding horizon control(RHC)method is introduced to enhance the accuracy of passenger flow control.And the genetic algorithm is used to solve the model.The results of the calculation verify the effectiveness of the proposed method.Finally,the passenger flow situation of a working day of line 9 of Shanghai urban rail transit on 2017 is analyzed,and it is found that the passenger flow situation of the optimized line is improved.