, Volume 19, Issue 1, pp 117–145 | Cite as

Spatio-temporal compression of trajectories in road networks

  • Iulian Sandu Popa
  • Karine Zeitouni
  • Vincent Oria
  • Ahmed Kharrat


With the proliferation of wireless communication devices integrating GPS technology, trajectory datasets are becoming more and more available. The problems concerning the transmission and the storage of such data have become prominent with the continuous increase in volume of these data. A few works in the field of moving object databases deal with spatio-temporal compression. However, these works only consider the case of objects moving freely in the space. In this paper, we tackle the problem of compressing trajectory data in road networks with deterministic error bounds. We analyze the limitations of the existing methods and data models for road network trajectory compression. Then, we propose an extended data model and a network partitioning algorithm into long paths to increase the compression rates for the same error bound. We integrate these proposals with the state-of-the-art Douglas-Peucker compression algorithm to obtain a new technique to compress road network trajectory data with deterministic error bounds. The extensive experimental results confirm the appropriateness of the proposed approach that exhibits compression rates close to the ideal ones with respect to the employed Douglas-Peucker compression algorithm.


Spatio-temporal data compression Lossy compression Deterministic error bounds Data models Moving objects 



This work was partially supported by the KISS ANR-11-INSE-005 grant. The authors would also like to thank the reviewers for their valuable suggestions that improved significantly this journal article.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Iulian Sandu Popa
    • 1
  • Karine Zeitouni
    • 2
  • Vincent Oria
    • 3
  • Ahmed Kharrat
    • 2
  1. 1.University of Versailles Saint-Quentin and INRIA Paris-RocquencourtVersaillesFrance
  2. 2.University of Versailles Saint-QuentinVersaillesFrance
  3. 3.New Jersey Institute of TechnologyNewarkUSA

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