A New Trajectory Similarity Measure for GPS Data

Anas Ismail; Antoine E. Vigneron

doi:10.1145/2833165.2833173

A New Trajectory Similarity Measure for GPS Data

Anas Ismail, Antoine E. Vigneron

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance. © 2015 ACM.

Original language	English (US)
Title of host publication	Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming - IWGS'15
Publisher	Association for Computing Machinery (ACM)
Pages	19-22
Number of pages	4
ISBN (Print)	9781450339711
DOIs	https://doi.org/10.1145/2833165.2833173
State	Published - Aug 8 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Anas Ismail was supported by KAUST base funding

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10.1145/2833165.2833173

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title = "A New Trajectory Similarity Measure for GPS Data",

abstract = "We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance. {\textcopyright} 2015 ACM.",

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AU - Vigneron, Antoine E.

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PY - 2016/8/8

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N2 - We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance. © 2015 ACM.

AB - We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance. © 2015 ACM.

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UR - http://dl.acm.org/citation.cfm?doid=2833165.2833173

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M3 - Conference contribution

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BT - Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming - IWGS'15

PB - Association for Computing Machinery (ACM)

ER -

A New Trajectory Similarity Measure for GPS Data

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