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Review and classification of trajectory summarisation algorithms: From compression to segmentation

Author

Listed:
  • Daniel Amigo
  • David Sánchez Pedroche
  • Jesús García
  • José Manuel Molina

Abstract

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.

Suggested Citation

  • Daniel Amigo & David Sánchez Pedroche & Jesús García & José Manuel Molina, 2021. "Review and classification of trajectory summarisation algorithms: From compression to segmentation," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    • Handle: RePEc:sae:intdis:v:17:y:2021:i:10:p:15501477211050729
    DOI: 10.1177/15501477211050729
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    References listed on IDEAS

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    1. Penghui Sun & Shixiong Xia & Guan Yuan & Daxing Li, 2016. "An Overview of Moving Object Trajectory Compression Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, May.
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