Spatio-temporal compression of trajectories in road networks

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

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)117-145
Number of pages29
JournalGeoInformatica
Volume19
Issue number1
DOIs
StatePublished - Jan 2014

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

Keywords

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

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