Chapter
Jan 13, 2020
Sixth International Conference on Transportation Engineering

Application of Hierarchical Clustering Based on Principal Component Analysis to Railway Station Classification

Publication: ICTE 2019

ABSTRACT

Railway station classification is an effective way to simplify train timetable planning under the condition of railway network. In this paper, a classification method framework of railway station is proposed, which combines principal component analysis with hierarchical clustering. Firstly, considering that there are many attribute indicators and some indicators may be correlated, a new set of indicators is obtained by using principal component analysis to aggregate and reduce the dimension of attribute indicators of railway station. Secondly, a hierarchical clustering method is used to cluster the reduced data set of new station attributes, and the result of station classification is obtained. Finally, taking Beijing-Shanghai high-speed railway as an example, this method is compared with the direct clustering method. The results show that the hierarchical clustering based on principal component analysis is better than the direct clustering method.

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ACKNOWLEDGEMENTS

This research was supported by the National Key R&D Program of China (2016YFC0802208), National Natural Science Foundation of China (Project No. 61703351), Sichuan Science and Technology Program (Project NO. 2018RZ0078, 2019JDR0211), Science and Technology Plan of China Railway Corporation (Project No.: P2018T001), Chengdu Soft Science Research Project (2017-RK00-00028-ZF, 2017-RK00-00378-ZF) and the Fundamental Research Funds for the Central Universities(682017CX022, 2682017CX018), Service Science and Innovation Key Laboratory of Sichuan Province (KL1701).

REFERENCES

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Information & Authors

Information

Published In

Go to ICTE 2019
ICTE 2019
Pages: 162 - 171
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2

History

Published online: Jan 13, 2020

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Authors

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Chang’an Xu [email protected]
School of Transportation and Logistics, National Railway Train Diagram Research and Training Center, and National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong Univ., Chengdu 610031, China (corresponding author). E-mail: [email protected]
Junjie Li
School of Transportation and Logistics, National Railway Train Diagram Research and Training Center, and National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong Univ., Chengdu 610031, China
Congcong Zou
School of Transportation and Logistics, National Railway Train Diagram Research and Training Center, and National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong Univ., Chengdu 610031, China
Shaoquan Ni
School of Transportation and Logistics, National Railway Train Diagram Research and Training Center, and National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong Univ., Chengdu 610031, China

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