Factor analysis of key nodes in urban rail network

This paper builds important evaluation indexes for urban rail transit network. Besides degrees and betweenness in complex network, I add PageRank value and passenger flow indicators in consideration of the urban rail transit network topology model to better evaluate relationships between stations in urban rail transit network and offer theoretical guarantee for risks. We provide factor analysis method to calculate the importance degree of each station in the networks. Then illustrated by the case of Beijing Railway, and we compare the results with K-means cluster analysis. The results show that the factor analysis and the cluster analysis has consistence characteristics in macroscopic, but factor analysis can reflect the over-rall properties of indicators better and possess the explicit presentation. It has better actual leading meanings.