Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm

  • Maribel Yasmina Santos
  • Joaquim P. Silva
  • João Moura-Pires
  • Monica Wachowicz
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses,in a first stage,the research challenge of developing an approach for the automated identification of traffic routes based on lusteringmotion vectors rather thanreconstructed trajectories.The immediate benefit of the proposed approach is to avoid the reconstruction of trajectoriesin terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time.For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promisingfor the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.


Movement data Motion vectors Clustering Density-based clustering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We would like to thank the Maritime Research Institute in the Netherlands, for making the data available for analysis under the MOVE EU Cost Action IC0903 (Knowledge Discovery from Moving Objects).


  1. Bhavsar, H. and Jivani, A. (2009) The Shared Nearest Neighbor Algorithm with Enclosures (SNNAE), Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering, IEEE, pp. 436-442.Google Scholar
  2. Bouguessa, M. (2011) A Practical Approach for Clustering Transaction Data, Proceeding of the 7th International Conference on Machine Learning and Data Mining, New York, August/September, LNAI 6871, Springer-Verlag.Google Scholar
  3. Chen, L., Özsu, M. and Oria, V. (2005) Robust and fast similarity search for moving object trajectories, Proceedings of the 2005 ACM SIGMOD international conference on Management of data - SIGMOD’05, ACM Press, New York, New York, USA.Google Scholar
  4. Ertoz, L., Steinbach, M. and Kumar, V. (2002) Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data, Proceedings of the Second SIAM International Conference on Data Mining, San Francisco.Google Scholar
  5. Giannotti, F., Nanni, M., Pedreschi, D., and Pinelli, F. (2007) Trajectory Pattern Mining, Proceedings of the Knowledge Discovery in Databases (KDD’07) Conference, San Jose, pp. 330-339.Google Scholar
  6. Giannotti, F. and Pedreschi, D. (2008) Mobility, Data Mining and Privacy: A Vision of Convergence. In: Giannotti, F. and Pedreschi, D. (Eds.): Mobility, Data Mining and Privacy, Springer-Verlag, pp. 1-11.Google Scholar
  7. Grabmeier, J. (2002) Techniques of Cluster Algorithms in Data Mining, Data Mining and Knowledge Discovery, 6(4), pp. 303-360.Google Scholar
  8. Jarvis, R. and Patrick, E. (1973) Clustering Using a Similarity Measure Based on Shared Near Neighbors, IEEE Transactions on Computers, C-22(11), pp. 1025-1034.Google Scholar
  9. Lee, J.-G., Han, J. and Whang, K.-Y. (2007) Trajectory Clustering: A Partition-and-Group Framework, Proceedings of SIGMOD Conference (SIGMOD’07), Beijing, pp. 593-604.Google Scholar
  10. Little, J. J. and Gu, Z. (2001) Video retrieval by spatial and temporal structure of trajectories, Proceedings of SPIE, The International Society for Optical Engineering, pp. 545-552.Google Scholar
  11. Meratnia, N. and de By, R. A. (2002) Aggregation and comparison of trajectories, Proceedings of the 10th ACM international symposium on Advances in Geographic Information Systems, ACM, pp. 49–54.Google Scholar
  12. Miller, H. J. and Han, J. (2009) Geographic Data Mining and Knowledge Discovery, 2nd edition, Taylor & Francis Group.Google Scholar
  13. Perez, H. M., Chang, R., Billings, R., and Kosub, T. L. (2009) Automatic Identification Systems (AIS) Data Use in Marine Vessel Emission Estimation, Presented at the 18th Annual International Emission Inventory Conference. Baltimore.Google Scholar
  14. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., and Andrienko, G. (2008) Visually driven analysis of movement data by progressive clustering, Information Visualization, 7, pp. 225-239.Google Scholar
  15. Vlachos, M., Kollios, G. and Gunopulos, D. (2002) Discovering similar multidimensional trajectories, Proceedings 18th International Conference on Data Engineering, IEEE Computer Society, San Jose, CA, USA, pp. 673-684.Google Scholar
  16. Zaït, M. and Messatfa, H. (1997) A comparative study of clustering methods, Future Generation Computer Systems, 13(2), pp. 149-159.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maribel Yasmina Santos
    • 1
  • Joaquim P. Silva
    • 2
  • João Moura-Pires
    • 3
  • Monica Wachowicz
    • 4
  1. 1.Algoritmi Research Centre, University of MinhoBarcelosPortugal
  2. 2.School of TechnologyPolytechnic Institute of Cávado and AvenueBarcelosPortugal
  3. 3.Faculty of Science and TechnologyNew University of LisbonLisbonPortugal
  4. 4.Geodesy and Geomatics EngineeringUniversity of New BrunswickFrederictonCanada

Personalised recommendations