Study On Passenger Flow Forecast Method Of Rail Transfer Station For Urumqi

The passenger flow prediction of urban rail transfer station is an important basis for the design of rail transfer station scale,personnel facilities configuration and flow organization scheme.the accuracy of passenger flow forecast affects the effectiveness of the construction of rail traffic directly.In this research,logit model for passenger flow forecast of rail transit passenger flow shift and transfer were established mainly considered the relationship of shift from other traffic to rail transit and transfer to rail transit,studied the method of the passenger flow forecast for rail transfer station,which aims to improve the accuracy of rail transit station passenger flow forecast.Firstly,the relevant research for the prediction of rail transit passenger flow at home and abroad have been analysed comprehensively,and a method to forecast the passenger rail transfer station using logit model basing the expected utility theory combining with the SP(Stated Preference Survey)data was propounded contraposing the shortcomings of existing research.After analyzing the influence factors,the traffic information,traffic environment,travel habits,transfer times,individual attributes and other factors on the passenger flow of rail transit transfer stations were comprehensively considered.Secondly,considering the behavior of travelers directly shift to rail transit from other traffic modes,which caused by the competitive relationship,the logit model of the passenger flow forecast was established.Through compared the superiority between Nested Logit Model(NL)and Mixed Logit Model(ML),the results showed that the estimation results of the ML is better,which can effectively reflect the preference randomness of travelers under the influence of the factors,and predict rail transit transfer station flow caused by the passenger transfer.the results also showed that traffic information,traffic environment,travel habits,travel time,travel costs,income were the main factors affecting the passenger flow of rail transit station.Considering the relationship between other traffic modes and rail transit,this paper established ML for predicting passenger flow of rail transit transfer.Through the comparative analysis,it was found that the ML model with the log normal distribution could better predict the passenger flow of the transfer station.The results showed that traffic information,travel time,travel cost,transfer times and income were the main factors affecting the transfer of passenger traffic.Finally,through the analysis and forecast of the South Gate transfer station passenger flow by two groups of OD(Origin Destination)data of Urumqi,The application of rail transfer station proposed by this study had been demonstrated.The purpose of this study was to improve the prediction accuracy of the passenger flow of rail transit transfer station,which laid the foundation for the follow-up study of rail transit passenger flow forecast,and had certain guiding significance to the construction and management of rail transit transfer station.