skip to main content
10.1145/3638985.3639023acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicitConference Proceedingsconference-collections
research-article

A Spatiotemporal Trajectory Similarity Measurement Based on Error Ellipses and Stability

Published: 11 March 2024 Publication History

Abstract

The measurement of trajectory similarity plays a crucial role in the processes of trajectory retrieval, classification, mining, and other trajectory analysis tasks, and it finds widespread application in trajectory data. Existing similarity measurement methods have mostly been developed under the assumption of good data quality, with a predominant focus on spatial aspects while seldom considering both spatial and temporal dimensions simultaneously. An essential challenge related to temporal considerations is dealing with trajectories that have different sampling rates and asynchronous sampling, both of which introduce a degree of uncertainty. To address these issues, this paper presents a novel method for similarity measurement, considering uncertainty, based on error ellipses, termed Spatio-Temporal Uncertain Trajectory Similarity Measurement (STUSM). Experimental comparisons were conducted using real trajectory data and related work. The results indicate that the proposed approach exhibits enhanced robustness when dealing with various challenges such as different sampling rates, asynchronous sampling, and outliers.

References

[1]
Cao S, Wu L, Wu J, A spatio-temporal sequence-to-sequence network for traffic flow prediction[J]. Information Sciences, 2022, 610: 185-203.
[2]
McLean D J, Skowron Volponi M A. trajr: an R package for characterisation of animal trajectories[J]. Ethology, 2018, 124(6): 440-448.
[3]
Bose R, Pintar A, Simiu E. A real time prediction methodology for hurricane evolution using LSTM recurrent neural networks[J]. Neural Computing and Applications, 2022, 34(20): 17491-17505.
[4]
Tao Y, Both A, Silveira R I, A comparative analysis of trajectory similarity measures[J]. GIScience & Remote Sensing, 2021, 58(5): 643-669.
[5]
Meenakshi D, Shanavas A R M. Novel Shared Input Based LSTM for Semantic Similarity Prediction[J]. Journal of Advances in Information Technology Vol, 2022, 13(4).
[6]
Shi W, Chen P, Shen X, An adaptive approach for modelling the movement uncertainty in trajectory data based on the concept of error ellipses[J]. International Journal of Geographical Information Science, 2021, 35(6): 1131-1154.
[7]
Furtado, A.S.; Alvares, L.O.C.; Pelekis, N.; Theodoridis, Y.; Bogorny, V. Unveiling movement uncertainty for robust trajectory similarity analysis. Int. J. Geogr. Inf. Sci. 2018, 32, 140–168.
[8]
Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases[C]//Foundations of Data Organization and Algorithms: 4th International Conference, FODO'93 Chicago, Illinois, USA, October 13–15, 1993 Proceedings 4. Springer Berlin Heidelberg, 1993: 69-84.
[9]
Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories[C]//Proceedings 18th international conference on data engineering. IEEE, 2002: 673-684.
[10]
Chen L, Özsu M T, Oria V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD international conference on Management of data. 2005: 491-502.
[11]
Chen L, Ng R. On the marriage of lp-norms and edit distance[C]//Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 2004: 792-803.
[12]
Ranu S, Deepak P, Telang A D, Indexing and matching trajectories under inconsistent sampling rates[C]//2015 IEEE 31st International conference on data engineering. IEEE, 2015: 999-1010.
[13]
Pelekis N, Kopanakis I, Marketos G, Similarity search in trajectory databases[C]//14th International Symposium on Temporal Representation and Reasoning (TIME'07). IEEE, 2007: 129-140.
[14]
Shang S, Chen L, Wei Z, Parallel trajectory similarity joins in spatial networks[J]. The VLDB Journal, 2018, 27(3): 395-420.
[15]
Zheng Y, Zhang L, Xie X, Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th international conference on World wide web. 2009: 791-800.
[16]
Yuan J, Zheng Y, Zhang C, T-drive: driving directions based on taxi trajectories[C]//Proceedings of the 18th SIGSPATIAL International conference on advances in geographic information systems. 2010: 99-108.
[17]
SU, Han, A survey of trajectory distance measures and performance evaluation. The VLDB Journal, 2020, 29: 3-32.
[18]
Naderivesal S, Kulik L, Bailey J. An effective and versatile distance measure for spatiotemporal trajectories[J]. Data Mining and Knowledge Discovery, 2019, 33: 577-606.

Index Terms

  1. A Spatiotemporal Trajectory Similarity Measurement Based on Error Ellipses and Stability

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIT '23: Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City
    December 2023
    266 pages
    ISBN:9798400709043
    DOI:10.1145/3638985
    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 the author(s) 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].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 March 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Error Ellipses
    2. Similarity Measurement
    3. Space-Time Trajectories
    4. Uncertainty

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICIT 2023
    ICIT 2023: IoT and Smart City
    December 14 - 17, 2023
    Kyoto, Japan

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 11
      Total Downloads
    • Downloads (Last 12 months)11
    • Downloads (Last 6 weeks)4
    Reflects downloads up to 19 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media