Research On Departure Station Choice Behaviors Of Urban Rail Transit Passengers Under Station Closure

With the rapid development of urban rail transit and the increase of passenger flow,urban rail transit plays an increasingly important role in easing urban congestion,but at the same time,due to various reasons,metro station closures are more and more frequent.To ensure the safe and orderly operation during station closure,the identification of the influence range of station closure and the passenger departure station choice behavior are very important research contents.This paper identifies the influence range of station closure from the macro perspective,and discusses the mechanism of passenger behavior within the influence range of station closure from the micro perspective,to provide an important basis for the subsequent accurate prediction and analysis of passenger flow and the formulation of passenger flow induction measures.The main work of this paper is as follows:(1)This paper describes the abnormal state of rail transit with known station closure,analyzes the influence range of station closure and the characteristics of passenger flow.The spatio-temporal characteristics of the departure station selection of urban rail transit passengers are briefly analyzed.From the perspective of passengers(micro),this paper studies the whole journey process of passengers during station closure,analyzes the influencing factors of departure station choice,and summarizes the process of urban rail transit passenger’s departure station choice.(2)A data-driven identification method for the influence range of metro station closure is studied.Based on AFC data,the anomaly identification is carried out from two dimensions,the number of inbound and outbound stations and OD travel time.and the degree of influence is classified from these two dimensions,to fully explore the internal correlation between AFC data and the scene of station closure.LOF algorithm,Grabus criterion,k NN algorithm are conbimed by weighting method and voting method were used to integrate independent sample T test,Wilcoxon Signed Rank Test and Mann-Whitney U test,respectively.And the effectiveness of the integrated method in the identification of the influence range of station closure was verified by a real case study.(3)The model of passenger departure station choice under station closure is studied.Firstly,the alternative departure station is determined according to the identified influence range and the attraction range of urban rail transit.Then,a random forest method was used to determine the key factors affecting the choice of departure station.According to the feasibility of the alternative departure station,a feasibility constraint was added to MDFT,and a passenger departure station choice behavior model was built based on MDFT.Finally,a maximum likelihood estimation parameter calibration method based on AFC data is proposed.(4)Taking the Olympic Park station of Beijing Subway Line 8 and Line 15 as a real case study,this paper carried on the identification of the influence range of the station closure and the modeling of the passenger departure station choice during morning rush hour.Firstly,the data-driven integration algorithm is used to identify the inbound traffic anomalies of 350 stations with 20 lines and the travel time anomalies of 23,809 OD pairs,and the results are compared and analyzed.Then,based on the identification of the influence range of the station closure,MDFT model and multi-logit(MNL)model are built to analyze the behavior of the passenger’s departure station choice during morning rush hour.Based on the indices and ROC curve,it is concluded that the estimation accuracy of MDFT is better than that of MNL.Finally,the MDFT model is used to predict the departure station choice of passengers during the station closure period,and some operation suggestions are proposed based on the predicted results.