Volume 8 Number 9 ( Sep. 2013)
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JSW 2013 Vol.8(9): 2289-2296 ISSN: 1796-217X
doi: 10.4304/jsw.8.9.2289-2296

Station Segmentation with an Improved K-Means Algorithm for Hangzhou Public Bicycle System

Haitao Xu1, Jing Ying2, Fei Lin1, Yubo Yuan3
1Hangzhou Dianzi University, Hangzhou, China
2Zhejiang University, Hangzhou, China
3East China University of Science and Technology, Shanghai, China

Abstract—In China, Hangzhou is the first city to establish the Public Bicycle System. Now, the system has been the largest bike- sharing program in the world. The software of Hangzhou Public Bicycle System was developed by our team. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, determining how to segment the stations into several sections to give different care is very important. In this paper, an improved k-means algorithm based on optimized simulated annealing is used to segment the stations of Hangzhou Public Bicycle System. The optimized simulated annealing(SA) algorithm is used to assign k-means initial cluster centers. Practice examples and comparison with the traditional k-means algorithm are made. The results show that the proposed algorithm is efficient and robust. The research result has been applied in Hangzhou.

Index Terms—Public Bicycle System, K-means algorithm, data mining, Segmentation.

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Cite: Haitao Xu, Jing Ying, Fei Lin, Yubo Yuan, "Station Segmentation with an Improved K-Means Algorithm for Hangzhou Public Bicycle System," Journal of Software vol. 8, no. 9, pp. 2289-2296, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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