Trajectory similarity measures

Author:

Toohey Kevin1,Duckham Matt1

Affiliation:

1. University of Melbourne, Australia

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Reference23 articles.

1. COMPUTING THE FRÉCHET DISTANCE BETWEEN TWO POLYGONAL CURVES

2. Detecting Commuting Patterns by Clustering Subtrajectories

3. Similarity of trajectories taking into account geographic context;Buchin M.;Journal of Spatial Information Science,2014

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