Congested Situation Identification of Urban Rail Transit Carriage Based on Deep Learning

摘要

There is congestion in urban rail transit carriage, which directly exerts an effect on the comfort of passengers and operational efficiency of urban transportation networks. Based on different physical and psychological requirements of passengers and the calculations on passengers' rate of mixture in urban rail transit carriage, with the investigation results of passengers' choice behavior of standing position, age, gender, and other indicators, density of standing passenger's evaluation criteria is established based on calculation of passengers mixed degree. To accurately identify the number of passengers, gender, and age in the key points and regions of carriage, the paper selects the method of regional probability estimation and deep learning. According to the output model, it can be judged whether or not the carriage is congested. The method can rapidly identify the congestion of carriage situation and determine whether the type of carriage congestion belongs to frequent or disequilibrium congestion.