Open Access

ARTICLE

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

Wei Li^{1, 2, 3}, Min Zhou^{1, *}, 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. https://doi.org/10.32604/iasc.2020.013930

Abstract

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.

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Keywords

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Citations