Abstract
Traffic data, automatically collected en masse every day, can be mined to discover information or patterns to support police investigations. Leveraging on domain expertise, in this paper we show how unsupervised clustering techniques can be used to infer trending behaviors for road-users and thus classify both routes and vehicles. We describe a tool devised and implemented upon openly-available scientific libraries and we present a new set of experiments involving three years worth data. Our classification results show robustness to noise and have high potential for detecting anomalies possibly connected to criminal activity.
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Guarino, S., Leuzzi, F., Lombardi, F., Mastrostefano, E. (2018). Traffic Data Classification for Police Activity. 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_17
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DOI: https://doi.org/10.1007/978-3-030-01851-1_17
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