Identifying crash type propensity using real-time traffic data on freeways

Z Christoforou (1) , S Cohen (1) , M Karlaftis (2)

1 IFSTTAR/GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée
2 NTUA - National Technical University of Athens [Athens]

We examine the effects of various traffic parameters on type of road crash. Multivariate Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. 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 congestions, while single-vehicle crashes appear to be largely geometry-dependent.

Mots clés: ACCIDENT DE LA ROUTE; ACCIDENT

Domaines: Autre

Z Christoforou, S Cohen, M Karlaftis. Identifying crash type propensity using real-time traffic data on freeways. Journal of Safety Research, 2011, 42 (42), pp 43-50. ⟨10.1016/j.jsr.2011.01.001⟩. ⟨hal-01221053⟩