zhang-anefficientmap-2015.pdf (6.57 MB)
An efficient MapReduce-based parallel clustering algorithm for distributed traffic subarea division
journal contribution
posted on 2015-01-01, 00:00 authored by D Xia, B Wang, Y Li, Z Rong, Zili ZhangZili ZhangTraffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.
History
Journal
Discrete dynamics in nature and societyVolume
2015Article number
793010Pagination
1 - 18Publisher
Hindawi Publishing Corp.Location
Cairo, EgyptPublisher DOI
ISSN
1026-0226eISSN
1607-887XLanguage
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, The AuthorsUsage metrics
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