@Article{iasc.2020.013930,
AUTHOR = {Wei Li, Min Zhou, Hairong Dong},
TITLE = {Classifications of Stations in Urban Rail Transit based on the Two-step Cluster},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {531--538},
URL = {http://www.techscience.com/iasc/v26n3/40012},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.013930}
}