Unsupervised Classification of Routes and Plates from the Trap-2017 Dataset

  • Massimo Bernaschi
  • Alessandro Celestini
  • Stefano Guarino
  • Flavio Lombardi
  • Enrico Mastrostefano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 728)

Abstract

This paper describes the efforts, pitfalls, and successes of applying unsupervised classification techniques to analyze the Trap-2017 dataset. Guided by the informative perspective on the nature of the dataset obtained through a set of specifically-written perl/bash scripts, we devised an automated clustering tool implemented in python upon openly-available scientific libraries. By applying our tool on the original raw data it is possibile to infer a set of trending behaviors for vehicles travelling over a route, yielding an instrument to classify both routes and plates. Our results show that addressing the main goal of the Trap-2017 initiative (“to identify itineraries that could imply a criminal intent”) is feasible even in the presence of an unlabelled and noisy dataset, provided that the unique characteristics of the problem are carefully considered. Albeit several optimizations for the tool are still under investigation, we believe that it may already pave the way to further research on the extraction of high-level travelling behaviors from gates transit records.

References

  1. 1.
    Sivaraman, S., and M.M. Trivedi. 2013. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems 14 (4): 1773–1795.CrossRefGoogle Scholar
  2. 2.
    Koller, D., J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell. 1994. Towards robust automatic traffic scene analysis in real-time. Proceedings of the 33rd IEEE Conference on Decision and Control, 3776–3781.Google Scholar
  3. 3.
    McLachlan, Geoffrey, and David Peel. 2004. Finite mixture models. New York: Wiley.MATHGoogle Scholar
  4. 4.
    Rubner, Yossi, Carlo Tomasi, and Leonidas J. Guibas. 2000. The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40 (2): 99–121.CrossRefMATHGoogle Scholar
  5. 5.
    Kullback, Solomon, and Richard A. Leibler. 1951. On information and sufficiency. The Annals of Mathematical Statistics 22 (1): 79–86.MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Joe, H. 1963. Ward Jr. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58 (301): 236–244.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Tanvi Jindal, Prasanna Giridhar, Lu An Tang, Jun Li, and Jiawei Han. 2013. Spatiotemporal periodical pattern mining in traffic data.Google Scholar
  8. 8.
    Elfeky, Mohamed G, Walid G Aref, and Ahmed K Elmagarmid. 2005. Periodicity detection in time series databases. IEEE Transactions on Knowledge and Data Engineering 17 (7): 875–887.CrossRefGoogle Scholar
  9. 9.
    Uday Kiran, R., Haichuan Shang, Masashi Toyoda, and Masaru Kitsuregawa. 2017. Discovering partial periodic itemsets in temporal databases. Proceedings of the 29th International Conference on Scientific and Statistical Database Management,SSDBM ’17, 30:1–30:6. New York: ACM.Google Scholar
  10. 10.
    Giannotti, Fosca, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, et al. 2011. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal 20 (5): 695–719.CrossRefGoogle Scholar
  11. 11.
    Emilian Necula. Analyzing Traffic Patterns on Street Segments Based on GPS Data Using R. Transportation Research Procedia, 10:276–285. 2015. 18th Euro Working Group on Transportation, EWGT 2015, 14–16 July 2015. The Netherlands: Delft.Google Scholar
  12. 12.
    Grossi, Valerio, Anna Monreale, Mirco Nanni, Dino Pedreschi, Franco Turini, et al. 2015. Clustering formulation using constraint optimization. Selected Papers of SEFM 2015 Workshop on Software Engineering and Formal Methods, 93–107. New York: Springer.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Massimo Bernaschi
    • 1
  • Alessandro Celestini
    • 1
  • Stefano Guarino
    • 1
  • Flavio Lombardi
    • 1
  • Enrico Mastrostefano
    • 1
  1. 1.Institute for Applied Computing (IAC-CNR)RomeItaly

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