Vers un modèle proactif pour identifier des risques de trafic

Our goal is to develop motorway traffic risks identification models using disaggregate traffic data, meteorological data and crash database for a study site at a two-lane-per-direction section on motorway A1 in Urtenen-Schönbühl, Switzerland. We define traffic situations - TSs representing traffic status for a certain time duration and traffic regimes - TRs obtained by clustering TSs. The models are traffic regimes – based and are developed using classification and regression trees to identify rear-end collision risks. Interpreting results shows that speed variance on the left lane and speed difference between two lanes are the two main causes of rear-end crashes.