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Driver Profile Detection Using Points of Interest Neighbourhood

C-ITS (Cooperative Intelligent Transport Systems) are growing very quickly in many parts over the world. Their benefits are of importance for fuel consumption, traffic management and road safety. Their deployments are in advanced steps in many countries. Their impacts on human life are not clearly known. For this reason, we propose to analyze a large set of data collected during real tests on open roads with many connected vehicles. This analysis allows us to focus on relevant information like driver profiles, abnormal driving behaviours, etc. In this paper, we present a methodology to analyze data provided by a real experimentation of C-ITS mobile stations. We mainly analyze the headings of each driver when approaching some Points of Interest (POI). We use unsupervised machine learning approaches to detect driver profiles. The interesting features about driver profiles obtained need to be enhanced and confirmed for larger data-sets.