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Temporal Similarity of Trajectories in Graphs

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Similarity Search and Applications (SISAP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12440))

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Abstract

The analysis of similar trajectories in a network provides useful information for different applications. In this study, we are interested in algorithms to efficiently retrieve similar trajectories. Many studies have focused on retrieving similar trajectories by extracting the geometrical and geographical information of trajectories. We provide a similarity function by making use of both the temporal aspect of trajectories and the structure of the underlying network. We propose an approximation technique offering the top-k similar trajectories with respect to a query in a specified time interval in an efficient way. We also investigate how our idea can be applied to similar behavior of the tourists, so as to offer a high-quality prediction of their next movements.

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Notes

  1. 1.

    https://snap.stanford.edu/data/ego-Facebook.html.

  2. 2.

    https://sobigdata.d4science.org/catalogue-sobigdata?path=/dataset/gps_track_milan_italy.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Pisa, Pisa, Italy

    Shima Moghtasedi

Authors
  1. Shima Moghtasedi
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Corresponding author

Correspondence to Shima Moghtasedi .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

  2. ISTI-CNR, Pisa, Italy

    Lucia Vadicamo

  3. University of Southern Denmark, Odense M, Denmark

    Arthur Zimek

  4. ISTI-CNR, Pisa, Italy

    Fabio Carrara

  5. University of Bologna, Bologna, Italy

    Ilaria Bartolini

  6. IT University of Copenhagen, Copenhagen, Denmark

    Martin Aumüller

  7. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  8. IT University of Copenhagen, Copenhagen, Denmark

    Rasmus Pagh

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Moghtasedi, S. (2020). Temporal Similarity of Trajectories in Graphs. In: Satoh, S., et al. Similarity Search and Applications. SISAP 2020. Lecture Notes in Computer Science(), vol 12440. Springer, Cham. https://doi.org/10.1007/978-3-030-60936-8_32

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  • DOI: https://doi.org/10.1007/978-3-030-60936-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60935-1

  • Online ISBN: 978-3-030-60936-8

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