Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

  • Mohamed Khalil El Mahrsi
  • Fabrice Rossi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7765)

Abstract

Clustering trajectory data attracted considerable attention in the last few years. Most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of road segments that are often travelled by the same trajectories. To achieve this end, we model the interactions between segments w.r.t. their similarity as a weighted graph to which we apply a community detection algorithm to discover meaningful clusters. We showcase our proposition through experimental results obtained on synthetic datasets.

Keywords

similarity clustering moving objects trajectories road network graph 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Giannotti, F., Pedreschi, D. (eds.): Mobility, Data Mining and Privacy - Geographic Knowledge Discovery. Springer (2008)Google Scholar
  2. 2.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)CrossRefGoogle Scholar
  3. 3.
    Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 660–671. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: SIGMOD 2007: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM, New York (2007)CrossRefGoogle Scholar
  5. 5.
    Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: ICDE 2008: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 1457–1459. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  6. 6.
    El Mahrsi, M.K., Rossi, F.: Modularity-Based Clustering for Network-Constrained Trajectories. In: Proceedings of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012, Bruges, Belgique, pp. 471–476 (April 2012)Google Scholar
  7. 7.
    Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB 2005, pp. 853–864. VLDB Endowment (2005)Google Scholar
  8. 8.
    Kharrat, A., Popa, I.S., Zeitouni, K., Faiz, S.: Clustering algorithm for network constraint trajectories. In: SDH. Lecture Notes in Geoinformation and Cartography, pp. 631–647. Springer (2008)Google Scholar
  9. 9.
    Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate gps trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2009, pp. 352–361. ACM, New York (2009)Google Scholar
  10. 10.
    Roh, G.-P., Hwang, S.-W.: NNCluster: An efficient clustering algorithm for road network trajectories. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010, Part II. LNCS, vol. 5982, pp. 47–61. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rossi, F., Villa-Vialaneix, N.: Représentation d’un grand réseau à partir d’une classification hiérarchique de ses sommets. Journal de la Société Française de Statistique 152(3), 34–65 (2011)MathSciNetGoogle Scholar
  13. 13.
    Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76(3) (September 2007)Google Scholar
  15. 15.
    Brinkhoff, T.: A framework for generating network-based moving objects. Geoinformatica 6, 153–180 (2002)CrossRefMATHGoogle Scholar
  16. 16.
    Schaeffer, S.E.: Graph clustering. Computer Science Review 1(1), 27–64 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Noack, A., Rotta, R.: Multi-level algorithms for modularity clustering. In: Vahrenhold, J. (ed.) SEA 2009. LNCS, vol. 5526, pp. 257–268. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mohamed Khalil El Mahrsi
    • 1
    • 2
  • Fabrice Rossi
    • 2
  1. 1.Département INFRESTélécom ParisTechParis cedex 13France
  2. 2.Équipe SAMM EA 4543Université Paris I Panthéon-SorbonneParis cedex 13France

Personalised recommendations