research-article

Trajectory similarity measures

Authors:

Matt DuckhamAuthors Info & Claims

SIGSPATIAL Special, Volume 7, Issue 1

Pages 43 - 50

https://doi.org/10.1145/2782759.2782767

Published: 20 May 2015 Publication History

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.

References

[1]

H. Alt and M. Godau. Computing the Fréchet distance between two polygonal curves. International Journal of Computational Geometry and Applications, 5(01n02):75--91, 1995.

[2]

D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359--370. Seattle, WA, 1994.

Digital Library

[3]

K. Buchin, M. Buchin, J. Gudmundsson, M. Löffler, and J. Luo. Detecting commuting patterns by clustering subtrajectories. In S.-H. Hong, H. Nagamochi, and T. Fukunaga, editors, Algorithms and Computation, volume 5369 of Lecture Notes in Computer Science, pages 644--655. Springer, 2008.

Digital Library

[4]

M. Buchin, S. Dodge, and B. Speckmann. Similarity of trajectories taking into account geographic context. Journal of Spatial Information Science, 9(1):101--124, 2014.

[5]

L. Chen and R. Ng. On the marriage of l_p-norms and edit distance. In Proc. 30th International Conference on Very Large Data Bases, pages 792--803, 2004.

Digital Library

[6]

L. Chen, M. T. Özsu, and V. Oria. Robust and fast similarity search for moving object trajectories. In Proc. ACM SIGMOD International Conference on Management of Data, SIGMOD '05, pages 491--502, New York, NY, USA, 2005. ACM.

Digital Library

[7]

S. Dodge. Exploring Movement Using Similarity Analysis. PhD thesis, Universität Zürich, 2011.

[8]

C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. SIGMOD Rec., 23(2):419--429, May 1994.

Digital Library

[9]

M. M. Fréchet. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo, 22(1):1--72, 1906.

[10]

J. Gudmundsson, P. Laube, and T. Wolle. Movement patterns in spatiotemporal data. In Encyclopedia of GIS, pages 726--732. Springer US, 2008.

[11]

J. Gudmundsson, P. Laube, and T. Wolle. Computational movement analysis. In Springer handbook of geographic information, pages 423--438. Springer, 2012.

[12]

J. Haase and U. Brefeld. Finding similar movements in positional data streams. In Proc. ECML/PKDD Workshop on Machine Learning and Data Mining for Sports Analytics, 2013.

[13]

E. Keogh and M. Pazzani. Scaling up dynamic time warping for datamining applications. In Proc. 6th ACM International Conference on Knowledge Discovery and Data Mining, pages 285--289, 2000.

Digital Library

[14]

E. Keogh and C. A. Ratanamahatana. Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3):358--386, 2005.

Digital Library

[15]

P. Laube and R. S. Purves. How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3):401--418, 2011.

[16]

T. Li, H. Chang, M. Wang, B. Ni, R. Hong, and S. Yan. Crowded scene analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology, (99):1--20, 2014.

[17]

M. Perše, M. Kristan, S. Kovačič, G. Vučkovič, and J. Perš. A trajectory-based analysis of coordinated team activity in a basketball game. Computer Vision and Image Understanding, 113(5):612--621, 2009. Computer Vision Based Analysis in Sport Environments.

Digital Library

[18]

P. Ranacher and K. Tzavella. How to compare movement? A review of physical movement similarity measures in geographic information science and beyond. Cartography and Geographic Information Science, 41(3):286--307, 2014.

[19]

K. Toohey. SimilarityMeasures: Trajectory Similarity Measures, 2015. R package version 1.4, http://CRAN.R-project.org/package=SimilarityMeasures.

[20]

M. Vlachos, G. Kollios, and D. Gunopulos. Discovering similar multidimensional trajectories. In Proc. 18th International Conference on Data Engineering (ICDE), pages 673--684, 2002.

Digital Library

[21]

H. Wang, H. Su, K. Zheng, S. Sadiq, and X. Zhou. An effectiveness study on trajectory similarity measures. In Proc. 24th Australasian Database Conference, volume 137, pages 13--22, 2013.

Digital Library

[22]

Y. Yuan. Image-based gesture recognition with support vector machines. ProQuest, 2008.

[23]

Z. Zhang, K. Huang, and T. Tan. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In Proc. 18th International Conference on Pattern Recognition (ICPR), volume 3, pages 1135--1138, 2006.

Digital Library

Cited By

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Fang JFeng CChao PXu JZhao PZhao L(2025)MDTRL: A Multi-Source Deep Trajectory Representation Learning for the Accurate and Fast Similarity QueryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351053426:2(2115-2128)Online publication date: 1-Feb-2025
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Trajectory similarity measures

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Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 7, Issue 1

March 2015

72 pages

EISSN:1946-7729

DOI:10.1145/2782759

Editor:
Chi-Yin (Ted) Chow
City University of Hong Kong

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Copyright © 2015 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2015

Published in SIGSPATIAL Volume 7, Issue 1

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Chen SGao MShi PZeng XZhang A(2025)Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation AssistanceJournal of Marine Science and Engineering10.3390/jmse1302036613:2(366)Online publication date: 16-Feb-2025
https://doi.org/10.3390/jmse13020366
Foley MLato KFuirst MVeit RCerrato RThorne L(2025)Spatial and temporal predictability drive foraging movements of coastal birdsMovement Ecology10.1186/s40462-025-00531-y13:1Online publication date: 1-Feb-2025
https://doi.org/10.1186/s40462-025-00531-y
Fang JFeng CChao PXu JZhao PZhao L(2025)MDTRL: A Multi-Source Deep Trajectory Representation Learning for the Accurate and Fast Similarity QueryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351053426:2(2115-2128)Online publication date: 1-Feb-2025
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