Efficient Point-Based Trajectory Search

  • Shuyao QiEmail author
  • Panagiotis Bouros
  • Dimitris Sacharidis
  • Nikos Mamoulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Trajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technology, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-based trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top-k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid method and also proposing an alternative, more efficient range-based approach. Second, we propose and study the practical variant of bounded distance-based search, which takes into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our range-based approach outperforms previous methods by at least one order of magnitude.


Near Neighbor Priority Queue Query Point Partial Match Trajectory Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuyao Qi
    • 1
    Email author
  • Panagiotis Bouros
    • 2
  • Dimitris Sacharidis
    • 3
  • Nikos Mamoulis
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongHong KongChina
  2. 2.Department of Computer ScienceHumboldt-Universität zu BerlinBerlinGermany
  3. 3.Faculty of InformaticsTechnische Universität WienWienAustria

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