Research on the Classification of Urban Rail Transit Stations - Taking Shanghai Metro as an Example

At present, the average daily passenger flow are more than tens of millions in workday in Shanghai rail transit. It is normal for office workers to experience the peak of morning and evening crowded. It also causes great difficulties for managers to operate. In order to optimize the operation of the subway services, improve the quality of the management of the subway, clustering analysis and classification management of the subway sites are done. This paper takes Shanghai subway as the research object, using the data for September 2016 from intelligent traffic card of Shanghai Subway, select the site features and the related 36 factors as the initial variables of cluster analysis, extract 5 main factors from the variables to reduce the variable dimensions, and the extracted main factors is used to cluster the sites and divide the Shanghai subway Line 359 subway stations into several sites types with different congestion degree by the K-means clustering method. Selecting site with high congestion degree as object of study, some suggestions of optimizing improvement are put forward.