Abstract
Urban rail transit has received widespread attention due to its advantages such as fast speed, high punctuality, green and low-carbon. To explore the characteristics of passengers in urban rail transit, a station classification method based on data mining is proposed. Using urban rail transit inbound and outbound swiping data, the station is divided into 5 categories using K-means clustering algorithm, with the inbound and outbound passenger flows of each station as variables during the three periods of the day, morning peak, and evening peak. The results show that the inbound and outbound passenger flow data can better reflect the spatiotemporal characteristics of different types of rail stations. Finally, the passenger travel characteristics of different types of stations are analysed, and the identification study of different types of stations can provide reference for the planning, design, and operation management of rail transit stations.
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