Research On The Prediction Of Passenger Departures And Transfer Volume Of Railway Passenge Transport Tub

The railway passenger transport hub is a comprehensive interchange hub with external and internal traffic functions.As a key component in the city’s integrated transportation system,it is also a key link that connects the internal and external transportation with the city’s public transportation.In recent years,with the continuous increase in passenger traffic of high-speed rail,especially during the peak period of train arrivals,the passenger traffic has increased dramatically,requiring the hub railway passenger station to be able to quickly evacuate a large number of passengers in the station.Therefore,it is necessary to study the methods and measures to improve the evacuation efficiency of passengers inside the station,that is,how to quickly move passengers to various transfer modes,reduce the passenger’s stay time in the station,and increase the passenger transfer speed of the railway passenger hub.Therefore,it is proposed to predict the passenger flow from the passenger terminal of the railway passenger interchange to various modes of transportation,to coordinate and coordinate the operation plan of each mode,to scientifically and comprehensively establish the internal organization and operation plan of the urban passenger terminal,and to rationally plan the layout of the interchange facilities within the hub Speed up the evacuation of passenger flow.This paper takes the passenger transfer from the railway passenger terminal as the research object,based on the analysis of hub characteristics and the connection of passenger station and urban traffic transfer,studies passenger transfer behavior based on the theory of planned behavior,and analyzes the influencing factors of passenger selection behavior.Then through the comparison of short-term passenger flow prediction methods,the GRU neural network passenger flow prediction method is used to predict the arrival passenger flow of railway hub stations.Using the analytic hierarchy process(AHP)to solve the satisfaction degree of the change to the environment,it with the personal characteristics and travel characteristics,change to the way together as a characteristic variable,set up passenger transfer mode choice of analytic hierarchy process(AHP)and reflect the passenger random preferences combinations of mixed Logit model,the utility function is improved,optimized the influence factors in the traditional model of characteristic variables and the method of transfer to share rate.Finally,the GRU neural network is used to predict the arrival passenger flow of Chengdu East Station.Based on the RP survey method,a day is divided into two periods to predict the transfer sharing rate of departing passengers at chengdu east railway station respectively,and the transfer amount between railway and various transportation modes is finally obtained,which verifies the effectiveness and reliability of the prediction method and model.Through comparison and analysis with the prediction of transfer volume in 2020 and 2030,the paper puts forward some suggestions on the evacuation control and organization of transfer passenger flow.