research-article

A New Trajectory Similarity Measure for GPS Data

Authors:

Antoine VigneronAuthors Info & Claims

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

Pages 19 - 22

https://doi.org/10.1145/2833165.2833173

Published: 03 November 2015 Publication History

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.

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Cited By

Bao PChen ZWang JDai DZhao H(2023)Lifelong Vehicle Trajectory Prediction Framework Based on Generative ReplayIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330054524:12(13729-13741)Online publication date: Dec-2023
https://doi.org/10.1109/TITS.2023.3300545
Ferreira MCampbell JPurney ESoares AMatwin S(2023)Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectoriesInternational Journal of Geographical Information Science10.1080/13658816.2022.216349437:3(660-683)Online publication date: 14-Feb-2023
https://doi.org/10.1080/13658816.2022.2163494
Liu YLiu ALiu GLi ZZhao L(2023)Towards Effective Trajectory Similarity Measure in Linear TimeDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_19(283-299)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-3-031-30637-2_19
Show More Cited By

Index Terms

A New Trajectory Similarity Measure for GPS Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Conferences

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

November 2015

102 pages

ISBN:9781450339711

DOI:10.1145/2833165

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2015

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SIGSPATIAL'15

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SIGSPATIAL

SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

WA, Bellevue, USA

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Cited By

Bao PChen ZWang JDai DZhao H(2023)Lifelong Vehicle Trajectory Prediction Framework Based on Generative ReplayIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330054524:12(13729-13741)Online publication date: Dec-2023
https://doi.org/10.1109/TITS.2023.3300545
Ferreira MCampbell JPurney ESoares AMatwin S(2023)Assessing compression algorithms to improve the efficiency of clustering analysis on AIS vessel trajectoriesInternational Journal of Geographical Information Science10.1080/13658816.2022.216349437:3(660-683)Online publication date: 14-Feb-2023
https://doi.org/10.1080/13658816.2022.2163494
Liu YLiu ALiu GLi ZZhao L(2023)Towards Effective Trajectory Similarity Measure in Linear TimeDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_19(283-299)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-3-031-30637-2_19
Yoo WKim T(2022)Statistical trajectory-distance metric for nautical route clustering analysis using cross-track distanceJournal of Computational Design and Engineering10.1093/jcde/qwac0249:2(731-754)Online publication date: 13-Apr-2022
https://doi.org/10.1093/jcde/qwac024
Ferreira MCampbell JMatwin S(2022)A novel machine learning approach to analyzing geospatial vessel patterns using AIS dataGIScience & Remote Sensing10.1080/15481603.2022.211843759:1(1473-1490)Online publication date: 20-Sep-2022
https://doi.org/10.1080/15481603.2022.2118437
Cai JChen GLützen MRytter N(2021)A practical AIS-based route library for voyage planning at the pre-fixture stageOcean Engineering10.1016/j.oceaneng.2021.109478236(109478)Online publication date: Oct-2021
https://doi.org/10.1016/j.oceaneng.2021.109478
Xiao PAng MJiawei ZLei W(2019)Approximate Similarity Measurements on Multi-Attributes Trajectories DataIEEE Access10.1109/ACCESS.2018.28894757(10905-10915)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2889475
Tong MTao YZhang YBian KYan W(2019)Trajectory-Based User Encounter Prediction Over Wireless Sensor NetworksWireless Personal Communications10.1007/s11277-019-06367-1Online publication date: 17-May-2019
https://doi.org/10.1007/s11277-019-06367-1
Su HLiu SZheng BZhou XZheng K(2019)A survey of trajectory distance measures and performance evaluationThe VLDB Journal10.1007/s00778-019-00574-9Online publication date: 18-Oct-2019
https://doi.org/10.1007/s00778-019-00574-9
Pesara APatil VAtrey P(2018)Secure Computing of GPS Trajectory SimilarityProceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks10.1145/3282825.3282832(1-7)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282825.3282832
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