American Society of Civil Engineers


Based on the Improved K-Means Algorithm of Tianjin Port Traffic Flow Characteristic Analysis


by Zhongzhen Yan, (Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, 430063, P. R. China; and Engineering Research Center for Transportation Safety (Ministry of Education) , Wuhan University of Technology, Wuhan, 430063, P. R. China. E-mail: fulianla@sohu.com), Xinping Yan, (Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, 430063, P. R. China; and Engineering Research Center for Transportation Safety (Ministry of Education) , Wuhan University of Technology, Wuhan, 430063, P. R. China), Ming Huang, (Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, 430063, P. R. China; and Engineering Research Center for Transportation Safety (Ministry of Education) , Wuhan University of Technology, Wuhan, 430063, P. R. China), Lei Xie, (Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, 430063, P. R. China; and Engineering Research Center for Transportation Safety (Ministry of Education) , Wuhan University of Technology, Wuhan, 430063, P. R. China), and Zheyue Wang, (Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, 430063, P. R. China; and Engineering Research Center for Transportation Safety (Ministry of Education) , Wuhan University of Technology, Wuhan, 430063, P. R. China)
Section: Volume I: Highway Transportation, pp. 1863-1870, (doi:  http://dx.doi.org/10.1061/41177(415)235)

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Document type: Conference Proceeding Paper
Part of: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation
Abstract: Mastering the characteristics of port ships traffic flow is the premise to plan, operate, and control waterway and navigation reasonably. Therefore, analysis of traffic flow characteristic of port ships is necessary and extremely important. Because Vessel traffic flow data acquisition methods are diverse, they contain flawed and incorrect data. To solve the problem, we made use of the improved K-means algorithm to prove the raw data of sequence samples. We applied data mining clustering analysis method to the Tianjin port ships traffic flow data characteristic for analysis. The results show that some valuable information is obtained by the proposed method which can also provide decision support for maritime safety administration.


ASCE Subject Headings:
Algorithms
Harbors
China
Traffic flow
Water transportation