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Meeting Pattern Detection from Trajectories in Road Network

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Web and Big Data (APWeb-WAIM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14962))

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Abstract

As technological advancements in positioning devices progress, the analysis of spatio-temporal trajectory data has become increasingly critical, particularly in identifying the group movement pattern of moving objects. However, existing studies on group convergence behavior in urban networks require absolute temporal continuity, which may lead to the loss of interesting patterns. To overcome this challenge, we introduce a new pattern, the meeting pattern, which relaxes the time constraints on the convergence behavior. To effectively detect meeting patterns, we design and implement two algorithms, the tree structure-based MT-MPM algorithm and the ID partition-based IDP-MPM algorithm. Extensive experiments conducted on three datasets not only validate the time characteristics of the meeting pattern, but also confirm the effectiveness and efficiency of MT-MPM and IDP-MPM algorithms.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/.

  2. 2.

    http://shanghai.sodachallenges.com/data.html.

  3. 3.

    https://iapg.jade-hs.de/personen/brinkhoff/generator.

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (62306266, 62276227, 62266050), the Program of Yunnan Key Laboratory of Intelligent Systems and Computing (202405AV340009), the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033) and the Postgraduate Research and Innovation Foundation of Yunnan University (TM-23236919).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Weijuan Zhao, Peizhong Yang & Hongmei Chen

  2. Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, 650091, China

    Peizhong Yang, Lizhen Wang & Hongmei Chen

  3. School of Science and Technology, Dianchi College, Kunming, 650228, China

    Lizhen Wang

Authors
  1. Weijuan Zhao
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  2. Peizhong Yang
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  3. Lizhen Wang
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  4. Hongmei Chen
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Corresponding author

Correspondence to Peizhong Yang .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  2. National University of Singapore, Queenstown, Singapore

    Anthony Tung

  3. Zhejiang Normal University, Jinhua, China

    Zhonglong Zheng

  4. University of New South Wales, Sydney, NSW, Australia

    Zhengyi Yang

  5. University of New South Wales, Sydney, NSW, Australia

    Xiaoyang Wang

  6. Zhejiang Normal University, Jinhua, China

    Hongjie Guo

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhao, W., Yang, P., Wang, L., Chen, H. (2024). Meeting Pattern Detection from Trajectories in Road Network. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14962. Springer, Singapore. https://doi.org/10.1007/978-981-97-7235-3_27

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  • DOI: https://doi.org/10.1007/978-981-97-7235-3_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-7234-6

  • Online ISBN: 978-981-97-7235-3

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