An efficient mapreduce-based parallel clustering algorithm for distributed traffic subarea division
type: Item
instance of: scholarly article
title: An efficient mapreduce-based parallel clustering algorithm for distributed traffic subarea division
zbMATH Open document ID: 1418.90080
DOI: 10.1155/2015/793010
author: Dawen Xia
author: Binfeng Wang
author: Zhuobo Rong
author: Zili Zhang
author: Yantao Li
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: Traffic 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.
Mathematics Subject Classification ID: 90B20
zbMATH DE Number: 7025494
Wikidata QID: Q59105448
describes a project that uses: ImageNet
describes a project that uses: clusfind
describes a project that uses: AlexNet
describes a project that uses: MapReduce
describes a project that uses: ParaKMeans
describes a project that uses: Hadoop
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/793010
OpenAlex ID: W1885124406
cites work: Q5617409
cites work: A clustering approach using cooperative artificial bee colony algorithm
cites work: An efficient enhanced \(k\)-means clustering algorithm
cites work: Finding Groups in Data
description: scientific article; zbMATH DE number 7025494