Metro Stations Classification Based on Clustering Analysis-A Case Study of Beijing Metro

Cluster analysis is a good tool to classify urban rail transit stations and figure out the difference between stations. By using clustering analysis, the intention of this paper is to find the difference between different kinds of metro stations. The method we used in this study is known as K-medoids, the input of which is decided by principal components analysis (PCA), and the efficiency of the K-medoids algorithm is guaranteed by a density-based method because it can select the best start values. The data used was the passenger entry flow of the metro network during five workdays of one week in Beijing, China. By applying this method on the data, the metro stations are clustered into six categories, and the stations are put on the map, so the difference between the stations was determined.