Trajectory similarity measures
Trajectory similarity measures
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DOI：
10.1145/2782759.2782767
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Kevin Toohey;M. Duckham
中科院分区：
文献类型：
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作者：
Kevin Toohey;M. Duckham
随着轨迹数据的可用性和数量的增加，存储、查询和分析轨迹变得越来越重要。轨迹分析的一个重要类别是计算轨迹相似性。本文介绍并比较了四种最常用的轨迹相似性度量：最长公共子序列（LCSS）、Fréchet距离、动态时间规整（DTW）和编辑距离。这四项措施已在新的开源R包中实现，可在CRAN上免费获得[19]。本文强调了这四个相似性措施之间的一些差异，使用真实的轨迹数据，除了指示一些重要的新兴应用程序的轨迹相似性测量。
Storing, querying, and analyzing trajectories is becoming increasingly important, as the availability and volumes of trajectory data increases. One important class of trajectory analysis is computing trajectory similarity. This paper introduces and compares four of the most common measures of trajectory similarity: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance. These four measures have been implemented in a new open source R package, freely available on CRAN [19]. The paper highlights some of the differences between these four similarity measures, using real trajectory data, in addition to indicating some of the important emerging applications for measurement of trajectory similarity.