Trajectory Clustering by Sampling and Density
Trajectory Clustering by Sampling and Density
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DOI：
10.4031/mtsj.48.6.8
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发表时间：
2014-11
影响因子：
0.8
通讯作者：
Jiacai Pan;Qings Jiang;Zheping Shao;姜青山
中科院分区：
文献类型：
--
作者：
Jiacai Pan;Qings Jiang;Zheping Shao;姜青山
移动物体的轨迹数据包含了大量的交通流信息。从这种特殊类型的数据中提取有价值的知识是非常重要的。轨迹聚类是完成这种提取的最广泛使用的方法之一。然而，目前的轨迹聚类的做法总是组相似的子轨迹，从轨迹分区，这些方法将因此失去作为一个整体的轨迹的重要信息。针对这一问题，提出了一种基于采样和密度的聚类算法，将相似的交通运动轨迹（汽车、轮船、飞机等）进行聚类。进一步分析交通流的特性。特别是，本文提出了一种新的技术，使用点采样测量轨迹之间的距离。该距离测量不划分轨迹，因此保存了这些轨迹的综合知识。这种轨迹聚类方法是一种新的适应基于密度的聚类算法的运动对象的轨迹。然后，本文采用熵理论作为启发式选择该算法的参数值和平方误差和的方法来衡量聚类质量。在真实的船舶轨迹数据上的实验表明，该算法在运行时间上优于经典方法TRACLUSS，并且在发现交通流模式方面效果良好，具有上级性能。
The trajectory data of moving objects contain huge amounts of information pertaining to traffic flow. It is incredibly important to extract valuable knowledge from this particular kind of data. Trajectory clustering is one of the most widely used approaches to complete this extraction. However, the current practice of trajectory clustering always groups similar subtrajectories that are partitioned from the trajectories; these methods would thus lose important information of the trajectory as a whole. To deal with this problem, this paper introduces a new trajectory-clustering algorithm based on sampling and density, which groups similar traffic movement tracks (car, ship, airplane, etc.) for further analysis of the characteristics of traffic flow. In particular, this paper proposes a novel technique of measuring distances between trajectories using point sampling. This distance measure does not divide the trajectory and thus conserves the integrated knowledge of these trajectories. This trajectory clustering approach is a new adaptation of a density-based clustering algorithm to the trajectories of moving objects. This paper then adopts the entropy theory as the heuristic for selecting the parameter values of this algorithm and the sum of the squared error method for measuring the clustering quality. Experiments on real ship trajectory data have shown that this algorithm is superior to the classical method TRACLUSS in the run time and that this method works well in discovering traffic flow patterns.