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