An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

作者：Xia Dawen; Wang Binfeng; Li Yantao; Rong Zhuobo; Zhang Zili^*

来源：Discrete Dynamics in Nature and Society, 2015, 2015: 793010.

DOI：10.1155/2015/793010

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 Map Reduce-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 Map Reduce 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.

• 出版日期2015
• 单位西南大学; 迪肯大学