Infoscience

Conference paper

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.

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