Co-Clustering Network-Constrained Trajectory Data

  • Mohamed K. El Mahrsi
  • Romain Guigourès
  • Fabrice Rossi
  • Marc Boullé
Part of the Studies in Computational Intelligence book series (SCI, volume 615)


Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network.


  1. Benkert, M., J. Gudmundsson, F. Hübner, and T. Wolle. 2006. Reporting flock patterns. In ESA’06: Proceedings of the 14th conference on annual European symposium, 660–671. London: Springer.Google Scholar
  2. Boullé, M. 2011. Data grid models for preparation and modeling in supervised learning. In Hands-on pattern recognition: Challenges in machine learning, vol. 1, 99–130. Microtome.Google Scholar
  3. Brinkhoff, T. 2002. A framework for generating network-based moving objects. Geoinformatica 6: 153–180.zbMATHCrossRefGoogle Scholar
  4. El Mahrsi, M.K., and F. Rossi. 2012a. Graph-based approaches to clustering network-constrained trajectory data. In Proceedings of the workshop on new frontiers in mining complex patterns (NFMCP 2012), 184–195. Bristol, Royaume-Uni.Google Scholar
  5. El Mahrsi, M.K., and F. Rossi. 2012b. Modularity-based clustering for network-constrained trajectories. In Proceedings of the 20-th European symposium on artificial neural networks, computational intelligence and machine learning (ESANN 2012), 471–476. Bruges, Belgique.Google Scholar
  6. Guigourès, R., M. Boullé, and F. Rossi. 2012. A triclustering approach for time evolving graphs. In ICDM workshops.Google Scholar
  7. Guo, D., S. Liu, and H. Jin. 2010. A graph-based approach to vehicle trajectory analysis. Journal of Location-Based Services 4: 183–199.CrossRefGoogle Scholar
  8. Hansen, P., and N. Mladenovic. 2001. Variable neighborhood search: Principles and applications. European Journal of Operational Research 130(3): 449–467.zbMATHMathSciNetCrossRefGoogle Scholar
  9. Hwang, J.-R., H.-Y. Kang, and K.-J. Li. 2005. Spatio-temporal similarity analysis between trajectories on road networks. In ER (workshops), Lecture notes in computer science, 280–289. Springer.Google Scholar
  10. Jeung, H., H.T. Shen, and X. Zhou. 2008. Convoy queries in spatio-temporal databases. In ICDE’08: Proceedings of the 2008 IEEE 24th international conference on data engineering, 1457–1459. Washington: IEEE Computer Society.Google Scholar
  11. Kalnis, P., P. Kalnis, N. Mamoulis, and S. Bakiras. 2005. On discovering moving clusters in spatio-temporal data. In SSTD, 364–381.Google Scholar
  12. Kharrat, A., I.S. Popa, K. Zeitouni, and S. Faiz. 2008. Clustering algorithm for network constraint trajectories. In SDH, Lecture notes in geoinformation and cartography, 631–647. Springer.Google Scholar
  13. Kharrat, A., I.S. Popa, K. Zeitouni, and S. Faiz. 2009. Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d’objets mobiles. In UbiMob, ed. D. Menga and F. Sedes, ACM international conference proceeding series, 33–40. ACM.Google Scholar
  14. Lee, J.-G., J. Han, and K.-Y. Whang. 2007. Trajectory clustering: A partition-and-group framework. In SIGMOD’07: Proceedings of the 2007 ACM SIGMOD international conference on management of data, 593–604. New York: ACM.Google Scholar
  15. Li, Y., J. Han, and J. Yang. 2004. Clustering moving objects. In KDD’04: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 617–622. New York: ACM.Google Scholar
  16. Liu, W., Z. Wang, and J. Feng. 2008. Continuous clustering of moving objects in spatial networks. In KES’08: Proceedings of the 12th international conference on knowledge-based intelligent information and engineering systems, part II, 543–550. Berlin: Springer.Google Scholar
  17. Lou, Y., C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. 2009. Map-matching for low-sampling-rate gps trajectories. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS’09, 352–361. New York: ACM.Google Scholar
  18. Meila, M., and J. Shi. 2000. Learning segmentation by Random Walks. In NIPS, 873–879.Google Scholar
  19. Noack, A. and R. Rotta. 2009. Multi-level algorithms for modularity clustering. In Proceedings of the 8th international symposium on experimental algorithms, SEA’09, 257–268. Berlin: Springer.Google Scholar
  20. Roh, G.-P., and S.-W. Hwang. 2010. Nncluster: An efficient clustering algorithm for road network trajectories. In Database systems for advanced applications, Lecture notes in computer science, vol. 5982, 47–61. Springer: Berlin.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohamed K. El Mahrsi
    • 1
  • Romain Guigourès
    • 2
  • Fabrice Rossi
    • 3
  • Marc Boullé
    • 2
  1. 1.Télécom ParisTech - Département Informatique et RéseauxParis Cedex 13France
  2. 2.Orange LabsLannionFrance
  3. 3.Équipe SAMM EA 4543, Université Paris I Panthéon-SorbonneParis Cedex 13France

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