Research On The Identification Of Interval Passenger Traffic Flow State In Urban Rail Transit

The state of interval passenger traffic flow in rail transit is the basis of passenger flow management for operation and management department of rail transit.Obtaining the information of interval passenger traffic flow state is beneficial to the decision-makers of rail transit to carry out operation adjustment and passenger flow guiding.The timely release of state information is beneficial to passengers’ choice of travel route.Scientific and reasonable identification of interval passenger traffic flow state can effectively improve the service level of rail transit passengers.Therefore,a real-time identification method of interval passenger traffic flow state in rail transit was constructed in this paper based on the characteristics of interval passenger traffic flow.The definition of interval passenger traffic flow state and the construction of the identification model of interval passenger traffic flow were mainly focused on in this paper.Firstly,the definition of interval passenger traffic flow was studied in this paper.The characteristics of interval passenger traffic flow were studied through related indexes of rail transit passenger flow.Through the analysis of passengers’ satisfaction with the level of train service,it was found that the degree of passenger congestion is the main index reflecting the state of interval passenger traffic flow.And through the study of relevant index of carriage congestion,the section full load rate was selected as the congestion index to reflect the state of interval passenger traffic flow.Finally,the evaluation standard of stand density was introduced.And according to the range of stand density under different comfort levels of the carriage,the threshold interval of section full load rate was divided into different levels through the transformation relationship between stand density and section full load rate.The interval passenger traffic flow was described qualitatively and quantitatively.Thus,the interval passenger traffic flow state was divided into five categories: free flow state,steady flow state,mild congestion state and moderate congestion state and severe congestion state.Secondly,the state identification model was constructed based on the relevance between interval passenger traffic flow fluctuation and operation time.In order to analyze the relevance of interval passenger traffic flow state,the ordered sample clustering method was used to divide the section passenger flow.The real-time section passenger flow estimation model under the condition of barrier-free transfer was used to calculate the section passenger flow of target interval in order to construct an ordered parameter sample.In order to keep the order of section passenger flow parameter samples from being disturbed in the process of classification,a calculation method of optimal classification number and the dynamic recursive strategy were used to improve the ordered sample clustering method.The section passenger flow with high correlation degree is classified into the same category.The interval passenger traffic flow state was identified based on the threshold interval of section full load rate of each category.The state identification model was applied to the interval of Zhangfuyuan to Xinjiekou in Nanjing Rail Transit Line 1.The result of example analysis shows that the model ensures the timing of section passenger flow data.It accords with the correlation characteristic of interval passenger traffic flow state,and reflects the continuity of interval passenger traffic flow state,which greatly reducing the discrete state point caused by passenger flow fluctuation.What’s more,the identification result has good stability and the state fluctuation is relatively small.This model of state identification has practical guiding significance.