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Dividing traffic sub-areas based on a parallel K-means algorithm
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posted on 2014-01-01, 00:00 authored by B Wang, L Tao, C Gao, D Xia, Z Rong, W Wang, Zili ZhangZili ZhangIn order to alleviate the traffic congestion and reduce the complexity of traffic control and management, it is necessary to exploit traffic sub-areas division which should be effective in planing traffic. Some researchers applied the K-Means algorithm to divide traffic sub-areas on the taxi trajectories. However, the traditional K-Means algorithms faced difficulties in processing large-scale Global Position System(GPS) trajectories of taxicabs with the restrictions of memory, I/O, computing performance. This paper proposes a Parallel Traffic Sub-Areas Division(PTSD) method which consists of two stages, on the basis of the Parallel K-Means(PKM) algorithm. During the first stage, we develop a process to cluster traffic sub-areas based on the PKM algorithm. Then, the second stage, we identify boundary of traffic sub-areas on the base of cluster result. According to this method, we divide traffic sub-areas of Beijing on the real-word (GPS) trajectories of taxicabs. The experiment and discussion show that the method is effective in dividing traffic sub-areas.
History
Title of book
Knowledge Science, Engineering and ManagementVolume
8793Series
Lecture Notes in Artificial IntelligenceChapter number
12Pagination
127 - 137Publisher
SpringerPlace of publication
Heidelberg, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319120966Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
34Editor/Contributor(s)
R Buchmann, C Kifor, J YuUsage metrics
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