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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24596
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dc.contributor.authorTao, Yaguang-
dc.contributor.authorBoth, Alan-
dc.contributor.authorSilveira, Rodrigo I.-
dc.contributor.authorBuchin, Kevin-
dc.contributor.authorSijben, Stef-
dc.contributor.authorPurves, Ross S.-
dc.contributor.authorLaube, Patrick-
dc.contributor.authorPeng, Dongliang-
dc.contributor.authorToohey, Kevin-
dc.contributor.authorDuckham, Matt-
dc.date.accessioned2022-03-17T07:59:27Z-
dc.date.available2022-03-17T07:59:27Z-
dc.date.issued2021-
dc.identifier.issn1548-1603de_CH
dc.identifier.issn1943-7226de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24596-
dc.description.abstractComputing trajectory similarity is a fundamental operation in movement analytics, required in search, clustering, and classification of trajectories, for example. Yet the range of different but interrelated trajectory similarity measures can be bewildering for researchers and practitioners alike. This paper describes a systematic comparison and methodical exploration of trajectory similarity measures. Specifically, this paper compares five of the most important and commonly used similarity measures: dynamic time warping (DTW), edit distance (EDR), longest common subsequence (LCSS), discrete Fréchet distance (DFD), and Fréchet distance (FD). The paper begins with a thorough conceptual and theoretical comparison. This comparison highlights the similarities and differences between measures in connection with six different characteristics, including their handling of a relative versus absolute time and space, tolerance to outliers, and computational efficiency. The paper further reports on an empirical evaluation of similarity in trajectories with contrasting properties: data about constrained bus movements in a transportation network, and the unconstrained movements of wading birds in a coastal environment. A set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity; c. assesses similarity with respect to known behavioral annotations (flight and foraging of oystercatchers); and d. compares bird and bus activity to examine whether they are distinguishable based solely on their movement patterns. The results show that in all instances both the absolute value and the ordering of similarity may be sensitive to the choice of measure. In general, all measures were more able to distinguish spatial differences in trajectories than temporal differences. The paper concludes with a high-level summary of advice and recommendations for selecting and using trajectory similarity measures in practice, with conclusions spanning our three complementary perspectives: conceptual, theoretical, and empirical.de_CH
dc.language.isoende_CH
dc.publisherTaylor & Francisde_CH
dc.relation.ispartofGIScience & Remote Sensingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectTrajectory similarityde_CH
dc.subjectMovement analyticsde_CH
dc.subjectSimilarity measurede_CH
dc.subjectNetwork-constrained movementde_CH
dc.subject.ddc500: Naturwissenschaftende_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleA comparative analysis of trajectory similarity measuresde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Umwelt und Natürliche Ressourcen (IUNR)de_CH
dc.identifier.doi10.1080/15481603.2021.1908927de_CH
dc.identifier.doi10.21256/zhaw-24596-
zhaw.funding.euNode_CH
zhaw.issue5de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end669de_CH
zhaw.pages.start643de_CH
zhaw.publication.statussubmittedVersionde_CH
zhaw.volume58de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Tao, Y., Both, A., Silveira, R. I., Buchin, K., Sijben, S., Purves, R. S., Laube, P., Peng, D., Toohey, K., & Duckham, M. (2021). A comparative analysis of trajectory similarity measures. GIScience & Remote Sensing, 58(5), 643–669. https://doi.org/10.1080/15481603.2021.1908927
Tao, Y. et al. (2021) ‘A comparative analysis of trajectory similarity measures’, GIScience & Remote Sensing, 58(5), pp. 643–669. Available at: https://doi.org/10.1080/15481603.2021.1908927.
Y. Tao et al., “A comparative analysis of trajectory similarity measures,” GIScience & Remote Sensing, vol. 58, no. 5, pp. 643–669, 2021, doi: 10.1080/15481603.2021.1908927.
TAO, Yaguang, Alan BOTH, Rodrigo I. SILVEIRA, Kevin BUCHIN, Stef SIJBEN, Ross S. PURVES, Patrick LAUBE, Dongliang PENG, Kevin TOOHEY und Matt DUCKHAM, 2021. A comparative analysis of trajectory similarity measures. GIScience & Remote Sensing. 2021. Bd. 58, Nr. 5, S. 643–669. DOI 10.1080/15481603.2021.1908927
Tao, Yaguang, Alan Both, Rodrigo I. Silveira, Kevin Buchin, Stef Sijben, Ross S. Purves, Patrick Laube, Dongliang Peng, Kevin Toohey, and Matt Duckham. 2021. “A Comparative Analysis of Trajectory Similarity Measures.” GIScience & Remote Sensing 58 (5): 643–69. https://doi.org/10.1080/15481603.2021.1908927.
Tao, Yaguang, et al. “A Comparative Analysis of Trajectory Similarity Measures.” GIScience & Remote Sensing, vol. 58, no. 5, 2021, pp. 643–69, https://doi.org/10.1080/15481603.2021.1908927.


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