CHARACTERIZING THE TRAFFIC DENSITY AND ITS EVOLUTION THROUGH MOVING OBJECT TRAJECTORIES

Ahmed Kharrat, Karine Zeitouni, Iulian Sandu-Popa, Sami Faiz

Abstract

Managing and mining data derived from moving objects is becoming an important issue in the last years. In this paper, we are interested in mining trajectories of moving objects such as vehicles in the road network. We propose a method for discovering dense routes by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatiotemporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph connecting dense routes over consecutive time periods. This graph is labeled by a degree of evolution. We have implemented and tested the proposed algorithms, which have shown their effectiveness and efficiency.

References

  1. Brinkhoff T., A Framework for Generating NetworkBased Moving Objects, GeoInformatica, Vol. 6, No. 2, Kluwer, 2002, pp. 153-180.
  2. Du Mouza C. and P. Rigaux. Mobility Patterns. In Proc. Intl. Workshop on Spatiotemporal Databases (STDBM'04).
  3. Ester M., H.-P. Kriegel, J. Sander and X. Xu (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. 2nd Int'l Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226-231.
  4. Lee J-G, J. Han and K-Y. Whang (2007) Trajectory Clustering: A Partition-and-Group Framework. In Proc. of SIGMOD'07, Beijing, China.
  5. Kharrat A., K. Zeitouni, I. Sandu-Popa and S. Faiz (2008) Clustering Algorithm for Network Constraint Trajectories, In 13th International Symposium on Spatial Data Handling, SDH, Montpellier, France, pp. 631-647.
  6. Li X., Han J., Lee J. and Gonzalez H. (2007) Traffic Density-Based Discovery of Hot Routes in Road Networks. In Proc. of the 10th International Symposium on Spatial and Temporal Databases (SSTD), Boston, pp. 441-459.
  7. Wan T. and Zeitouni K., An OLAP System for NetworkConstraint Moving Objects. In Proc. of the 22nd Annual ACM Symposium on Applied Computing (SAC'07), Seoul, Korea, pp. 13-18.
Download


Paper Citation


in Harvard Style

Kharrat A., Zeitouni K., Sandu-Popa I. and Faiz S. (2009). CHARACTERIZING THE TRAFFIC DENSITY AND ITS EVOLUTION THROUGH MOVING OBJECT TRAJECTORIES . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 319-322. DOI: 10.5220/0002309403190322


in Bibtex Style

@conference{kdir09,
author={Ahmed Kharrat and Karine Zeitouni and Iulian Sandu-Popa and Sami Faiz},
title={CHARACTERIZING THE TRAFFIC DENSITY AND ITS EVOLUTION THROUGH MOVING OBJECT TRAJECTORIES},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={319-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002309403190322},
isbn={978-989-674-011-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - CHARACTERIZING THE TRAFFIC DENSITY AND ITS EVOLUTION THROUGH MOVING OBJECT TRAJECTORIES
SN - 978-989-674-011-5
AU - Kharrat A.
AU - Zeitouni K.
AU - Sandu-Popa I.
AU - Faiz S.
PY - 2009
SP - 319
EP - 322
DO - 10.5220/0002309403190322