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

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.

Research Highlights
► Crash outcome can be defined by the traffic conditions before its occurrence.
► Rear-ends involving two vehicles are more probable under low speed and density.
► Rear-ends involving more than two vehicles are more probable under congestion.
► Two-vehicle sideswipe accidents are more probable with increasing volume.
► Multi-vehicle sideswipes are associated with high speeds, daytime, and flat freeways.