一种移动物体时空轨迹聚类的相似性度量方法  

A Similarity Measurement Method for Clustering Spatio-Temporal Trajectories of Moving Objects

作　　者：赵秀丽[1,2] 徐维祥[3] 

机构地区：[1]轨道交通控制与安全国家重点实验室,北京100044 [2]山东轻工业学院商学院,山东济南250353 [3]北京交通大学交通运输学院,北京100044

出　　处：《信息与控制》2012年第1期63-68,共6页Information and Control

基　　金：轨道交通控制与安全国家重点实验室资助项目(RCS2009ZT007);北京市科委资助项目(Z090506006309011);国家科技支撑计划资助项目(2009BAG12A10)

摘　　要：针对利用最小包围盒(MBB)压缩的移动物体时空轨迹,为了能对其进行有效地聚类,提出了一个基于盒内数据点密度的轨迹间相似性度量公式.首先,把两条轨迹的相似性度量转化为两条轨迹上有时间交叠的MBB之间的相似性度量,这在很大程度上减少了数据存储量.其次,分析两条轨迹上有时间交叠的MBB之间影响相似性的因素:时间持续、空间距离和盒内数据点的密度.剖析这3个因素对轨迹相似性的影响作用,提出了利用MBB压缩的移动物体时空轨迹相似性度量公式.实验证明采用本公式对移动物体时空轨迹进行聚类,可以提高聚类结果有效性指标Dunn的值.A similarity measurement formula is proposed based on the density of data points inside the boxes in order to effectively cluster the spatio-temporal trajectories of moving objects which are compressed into minimum bounding boxes （MBBs）. The similarity measurement of the raw trajectories is translated into the similarity measurement of the MBB sequences with time overlapping in two trajectories firstly, which reduces data storage volume to a great extent. Then some factors affecting the similarity of MBB sequences are analyzed, including the time duration, the space distance and the density of data points inside the boxes. Through analyzing the influence of the three factors on the trajectory similarity, a formula of the spatio-temporal trajectories compressed into MBB is compressed into MBB is proposed. Experiments show that the formula can improve the value of validity index Dunn when it is used to cluster the spatio-temporal trajectories of moving objects.

关 键 词：时空数据挖掘 移动物体轨迹 轨迹聚类 轨迹相似性度量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]