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Identifying crash type propensity using real-time traffic data on freeways
Zoi Christoforou1, Simon Cohen, Matthew G Karlaftis
1Université Paris-Est, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), «Le Descartes 2», 2 Rue de la Butte Verte, 93166 Noisy-Le-Grand, France. zoic@civil.ntua.gr
Introduction:
We examine the effects of various traffic parameters on type of road crash.
Method:
Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France.
Results:
Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent.
Impact On Industry:
Results could be integrated in a real-time traffic management application.
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