A dynamic identification method of passenger flow congestion risk in rail transit hub station based on grey clustering

Abstract

The rapid development of rail transit in big cities caues the increase of passenger flow year by year, and hub stations have become the main distributing center of massive passenger flow, leading to potential safety hazard of passenger flow congestion. The purpose of the paper is to study on the dynamic identification method of passenger flow congestion risk in rail transit hub station. Based on the real-time data of AFC (Automatic Fare Collection) system, the passenger flow of rail transit hub station was extracted, and the passenger flow was divided into three types: arrival passenger flow, departure passenger flow and transfer passenger flow based on the analysis of time-varying characteristics, choosing passenger flow of three types as evaluation indexes. Then the operation time (5:00 to 23:00) of rail transit was divided into 72 periods of time with the smallest unit in 15 minutes, and the evaluation model of passenger flow congestion risk in rail transit hub station was established based on grey clustering, realizing dynamic identification to passenger flow congestion risk level of rail transit hub station in different periods of time. Finally, the method was applied to evaluate the passenger flow congestion risk of Dongzhimen hub station, verifying the validity of the method.