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Mining Frequent Trajectory Patterns in Road Network Based on Similar Trajectory

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Intelligent Data Engineering and Automated Learning – IDEAL 2016 (IDEAL 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9937))

Abstract

Mining Trajectory Patterns plays an important role in moving objects. In many practical applications, the movement of objectives is always constrained by spatial space (e.g. road network). Therefore, it has more realistic significance to work on frequent trajectory patterns. This paper proposes a frequent trajectory patterns mining algorithm based on similar trajectory (named SimTraj-PrefixSpan). Since the trajectory data with the same frequent trajectory pattern may be not exactly the same, a trajectory similarity is utilized to measure whether considered trajectories have the same pattern. Computational results on simulated data and real data verify that the proposed algorithm can mine more complete and continuous frequent trajectory patterns, and shows the superiority of the proposed algorithm over traditional trajectory patterns mining algorithms in terms of mining efficiency.

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Acknowledgements

The research work was supported by National Natural Science Foundation of China (U1433116).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing, 211106, Jiangsu, People’s Republic of China

    Ming Qiu & Dechang Pi

Authors
  1. Ming Qiu
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  2. Dechang Pi
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Corresponding author

Correspondence to Dechang Pi .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. Nanjing University, Nanjing, China

    Yang Gao

  3. Yangzhou University, Yangzhou, Jiangsu, China

    Bin Li

  4. Aeronautics and Astronautics, Nanjing University Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. Nanjing Normal University, Nanjing, China

    Ming Yang

  6. Yangzhou University, Yangzhou, Jiangsu, China

    Yun Li

  7. Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Qiu, M., Pi, D. (2016). Mining Frequent Trajectory Patterns in Road Network Based on Similar Trajectory. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-46257-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46256-1

  • Online ISBN: 978-3-319-46257-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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