Dividing traffic sub-areas based on a parallel K-means algorithm

Binfeng Wang; Li Tao; Chao Gao; Dawen Xia; Zhuobo Rong; Wu Wang; Zili Zhang

doi:10.1007/978-3-319-12096-6_12

Dividing traffic sub-areas based on a parallel K-means algorithm

Binfeng Wang
, Li Tao
, Chao Gao
, Dawen Xia
, Zhuobo Rong
, Wu Wang
, Zili Zhang

Southwest University
Guizhou Minzu University
Deakin University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings
Editors	Robert Buchmann, Claudiu Vasile Kifor, Jian Yu
Publisher	Springer Verlag
Pages	127-137
Number of pages	11
ISBN (Electronic)	9783319120959
DOIs	https://doi.org/10.1007/978-3-319-12096-6_12
State	Published - 2014
Externally published	Yes
Event	7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014 - Sibiu, Romania Duration: 16 Oct 2014 → 18 Oct 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8793
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014
Country/Territory	Romania
City	Sibiu
Period	16/10/14 → 18/10/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

GPS trajectories
K-means
MapReduce
Traffic sub-areas

Access to Document

10.1007/978-3-319-12096-6_12

Cite this

Wang, B., Tao, L., Gao, C., Xia, D., Rong, Z., Wang, W., & Zhang, Z. (2014). Dividing traffic sub-areas based on a parallel K-means algorithm. In R. Buchmann, C. V. Kifor, & J. Yu (Eds.), Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings (pp. 127-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8793). Springer Verlag. https://doi.org/10.1007/978-3-319-12096-6_12

Wang, Binfeng ; Tao, Li ; Gao, Chao et al. / Dividing traffic sub-areas based on a parallel K-means algorithm. Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings. editor / Robert Buchmann ; Claudiu Vasile Kifor ; Jian Yu. Springer Verlag, 2014. pp. 127-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Dividing traffic sub-areas based on a parallel K-means algorithm",

abstract = "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.",

keywords = "GPS trajectories, K-means, MapReduce, Traffic sub-areas",

author = "Binfeng Wang and Li Tao and Chao Gao and Dawen Xia and Zhuobo Rong and Wu Wang and Zili Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014 ; Conference date: 16-10-2014 Through 18-10-2014",

year = "2014",

doi = "10.1007/978-3-319-12096-6\_12",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Wang, B, Tao, L, Gao, C, Xia, D, Rong, Z, Wang, W & Zhang, Z 2014, Dividing traffic sub-areas based on a parallel K-means algorithm. in R Buchmann, CV Kifor & J Yu (eds), Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8793, Springer Verlag, pp. 127-137, 7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014, Sibiu, Romania, 16/10/14. https://doi.org/10.1007/978-3-319-12096-6_12

Dividing traffic sub-areas based on a parallel K-means algorithm. / Wang, Binfeng; Tao, Li; Gao, Chao et al.
Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings. ed. / Robert Buchmann; Claudiu Vasile Kifor; Jian Yu. Springer Verlag, 2014. p. 127-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8793).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dividing traffic sub-areas based on a parallel K-means algorithm

AU - Wang, Binfeng

AU - Tao, Li

AU - Gao, Chao

AU - Xia, Dawen

AU - Rong, Zhuobo

AU - Wang, Wu

AU - Zhang, Zili

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - GPS trajectories

KW - K-means

KW - MapReduce

KW - Traffic sub-areas

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U2 - 10.1007/978-3-319-12096-6_12

DO - 10.1007/978-3-319-12096-6_12

M3 - 会议稿件

AN - SCOPUS:84909643036

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 127

EP - 137

BT - Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings

A2 - Buchmann, Robert

A2 - Kifor, Claudiu Vasile

A2 - Yu, Jian

PB - Springer Verlag

T2 - 7th International Conference on Knowledge Science, Engineering and Management, KSEM 2014

Y2 - 16 October 2014 through 18 October 2014

ER -

Wang B, Tao L, Gao C, Xia D, Rong Z, Wang W et al. Dividing traffic sub-areas based on a parallel K-means algorithm. In Buchmann R, Kifor CV, Yu J, editors, Knowledge Science, Engineering and Management - 7th International Conference, KSEM 2014, Proceedings. Springer Verlag. 2014. p. 127-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-12096-6_12

Dividing traffic sub-areas based on a parallel K-means algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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