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Towards Effective Trajectory Similarity Measure in Linear Time

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13943))

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Abstract

With the utilization of GPS devices and the development of location-based services, a massive amount of trajectory data has been collected and mined for many applications. Trajectory similarity computing, which identifies the similarity of given trajectories, is the fundamental functionality of trajectory data mining. The challenge in trajectory similarity computing comes from the noise in trajectories. Moreover, processing such a myriad of data also demands efficiency. However, existing trajectory similarity measures can hardly keep both accuracy and efficiency. In this paper, we propose a novel trajectory similarity measure termed ITS, which is robust to noise and can be evaluated in linear time. ITS converts trajectories into fixed-length vectors and compares them based on their respective vectors’ distance. Furthermore, ITS utilizes interpolation to get fixed-length vectors in linear time. The robustness of ITS owes to the interpolation, which makes trajectories aligned and points in trajectories evenly distributed. Experiments with 12 baselines on four real-world datasets show that ITS has the best overall performance on five representative downstream tasks in trajectory computing.

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Acknowledgements

This work is supported by Natural Science Foundation of Jiangsu Province (Grant Nos. BK20211307), and by project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yuanjun Liu, An Liu & Lei Zhao

  2. School of Computing, Macquarie University, Macquarie Park, Australia

    Guanfeng Liu

  3. School of Computer Science and Technology, Fudan University, Shanghai, China

    Zhixu Li

Authors
  1. Yuanjun Liu
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  2. An Liu
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  3. Guanfeng Liu
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  4. Zhixu Li
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  5. Lei Zhao
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Corresponding author

Correspondence to An Liu .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Liu, Y., Liu, A., Liu, G., Li, Z., Zhao, L. (2023). Towards Effective Trajectory Similarity Measure in Linear Time. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_19

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  • DOI: https://doi.org/10.1007/978-3-031-30637-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30636-5

  • Online ISBN: 978-3-031-30637-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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