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

机译：基于MapReduce的高​​效分布式交通分区划分并行聚类算法

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摘要

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-PhaseK-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 ofK-Means and then employ a MapReduce paradigm to redesign the optimizedK-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 withK-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.
机译：交通分区划分对于智能交通系统（ITS）中的交通系统管理和交通网络分析至关重要。由于现有方法可能不适用于大流量数据处理，因此本文提出了一种基于MapReduce的并行三相均值（Par3PKM）算法，用于在广泛采用的Hadoop分布式计算平台上解决流量分区划分问题。具体来说，我们首先修改K-Means的距离度量和初始化策略，然后使用MapReduce范式重新设计用于大规模滑行轨迹的并行聚类的优化K-Means算法。此外，我们提出了一种边界识别方法来连接每个聚类的聚类结果的边界。最后，我们使用拟议的方法，根据12,000辆出租车在一个月内生成的真实世界轨迹数据集，对北京的交通分区进行了划分。实验评估结果表明，与K-Means，Par2PK-Means和ParCLARA相比，Par3PKM具有更高的效率，更高的准确性和更好的可伸缩性，并且可以有效地划分具有较大滑行轨迹数据的交通区域。

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