Dividing traffic sub-areas based on a parallel K-means algorithm
Contribuinte(s) |
Buchmann,R Kifor,CV Yu,J |
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Data(s) |
01/01/2014
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Resumo |
In 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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30071839/wang-dividingtrafficsubareas-2014.pdf http://dro.deakin.edu.au/eserv/DU:30071839/wang-evid-bklnaivol8793-2014.pdf http://www.dx.doi.org/10.1007/978-3-319-12096-6 |
Direitos |
2014, Springer |
Palavras-Chave | #GPS trajectories #K-means #MapReduce #Traffic sub-areas |
Tipo |
Book Chapter |