Chapter
Jul 2, 2019
Metro Stations Classification Based on Clustering Analysis—A Case Study of Beijing Metro
Authors: Dongliang Lu [email protected], Min He [email protected], and Chunyan Shuai [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
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.
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Published In
CICTP 2019
Pages: 1707 - 1717
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© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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Affiliations
Faculty of Transportation Engineering, Kunming Univ. of Science and Technology, Yunnan, Kunming 650500, China. E-mail: [email protected]
Faculty of Transportation Engineering, Kunming Univ. of Science and Technology, Yunnan, Kunming 650500, China. E-mail: [email protected]
Faculty of Transportation Engineering, Kunming Univ. of Science and Technology, Yunnan, Kunming 650500, China. E-mail: [email protected]
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