Abstract
Since road traffic is nowadays predominant, improving its safety, security and comfortability may have a significant positive impact on people’s lives. This objective requires suitable studies of traffic behavior, to help stakeholders in obtaining non-trivial information, understanding the traffic models and plan suitable actions. While, on one hand, the pervasiveness of georeferencing and mobile technologies allows us to know the position of relevant objects and track their routes, on the other hand the huge amounts of data to be handled, and the intrinsic complexity of road traffic, make this study quite difficult. Deep Neural Networks (NNs) are powerful models that have achieved excellent performance on many tasks. In this paper we propose a sequence-to-sequence (Seq2Seq) autoencoder able to detect anomalous routes and consisting of an encoder Long Short Term Memory (LSTM) mapping the input route to a vector of a fixed length representation, and then a decoder LSTM to decode back the input route. It was applied to the TRAP2017 dataset freely available from the Italian National Police.
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Bernaschi, M., Celestini, A., Guarino, S., Lombardi, F., Mastrostefano, E.: Traffic data: exploratory data analysis with Apache Accumulo. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 129–143. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_10
Bernaschi, M., Celestini, A., Guarino, S., Lombardi, F., Mastrostefano, E.: Unsupervised classification of routes and plates from the TRAP 2017 dataset. In: Leuzzi and Ferilli [8], pp. 97–114
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Ferilli, S., Redavid, D.: A process mining approach to the identification of normal and suspect traffic behavior. In: Leuzzi and Ferilli [8], pp. 37–56
Fumarola, F., Lanotte, P.F.: Exploiting recurrent neural networks for gate traffic prediction. In: Leuzzi and Ferilli [8], pp. 145–153
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Leuzzi, F., Ferilli, S. (eds.): 1st Italian Conference for the Traffic Police (TRAP-2017). Advances in Intelligent Systems and Computing. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-75608-0
Leuzzi, F., Del Signore, E., Ferranti, R.: Towards a pervasive and predictive traffic police. In: Leuzzi and Ferilli [8], pp. 19–36
Liao, J., Zhao, X., Tang, J., Zhang, C., He, M.: Efficient and accurate traffic flow prediction via fast dynamic tensor completion. In: Leuzzi and Ferilli [8], pp. 69–82
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015). https://doi.org/10.1109/TITS.2014.2345663
Nanni, M.: Advancements in mobility data analysis. In: Leuzzi and Ferilli [8], pp. 11–16
Rodriguez-Jimenez, J.: Detecting criminal behaviour patterns in Spain and Italy using formal concept analysis. In: Leuzzi and Ferilli [8], pp. 57–68
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS 2014, pp. 3104–3112 (2014)
Acknowledgments
Work partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.
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Di Mauro, N., Ferilli, S. (2018). Unsupervised LSTMs-based Learning for Anomaly Detection in Highway Traffic Data. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_27
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DOI: https://doi.org/10.1007/978-3-030-01851-1_27
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