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Classifications of Stations in Urban Rail Transit based on the Two-step Cluster

Wei Li1, 2, 3, Min Zhou1, *, Hairong Dong1

1 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;
2 Beijing Transportation Information Center, Beijing 100073, China;
3 Beijing Key Laboratory of Comprehensive Transportation Operations and Service, Beijing 100073, China.

* Corresponding Author: Min Zhou,

Intelligent Automation & Soft Computing 2020, 26(3), 531-538.


Different types of stations have different functional roles in the urban rail transit network. Firstly, based on the characteristics of the urban rail transit network structure, the time series features and passenger flow features of the station smart card data are extracted. Secondly, we use the principal component analysis method to select the suitable clustering variables. Finally, we propose a station classification model based on the two-step cluster method. The effectiveness of the proposed method is verified in the Beijing subway. The results show that the proposed model can successfully identify the types of urban rail transit stations, clarify the function and orientation of each station.


Cite This Article

W. Li, M. Zhou and H. Dong, "Classifications of stations in urban rail transit based on the two-step cluster," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 531–538, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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