Pham, Minh-HaiBhaskar, AshishChung, EdwardDumont, André-Gilles2010-06-042010-06-042010-06-04201010.1049/cp.2010.0393https://infoscience.epfl.ch/handle/20.500.14299/50658This paper presents a methodology to develop motorway traffic risk identification models using individual vehicle traffic data, meteorological data and crash database for a study site at a two-lane-per-direction section on motorway A1 in Switzerland. We define traffic situations (TSs) representing traffic status for three-minute interval and traffic regimes obtained by clustering TSs. The models are traffic regimes – based and are developed using Regression Trees to identify rear-end collision risks. Interpreting results shows that speed variance on the right lane and speed difference between two lanes are the two main causes of rear-end crashes. We also compare the results obtained from three-minute TSs with the results obtained from five-minute TSs using the same methodology.Risk-sensitive active traffic managementtraffic situationtraffic regimerear-end crashtext::conference output::conference paper not in proceedings