Modelling and inference of relative collision safety in cars

Abstract: We propose a new mathematical model for relative collision safety in cars. Our present research is restricted to head-on crashes between two cars and we try to determine how much of the injury risk in a crash that depends on car model. The relative risks include the driver populations of the different car models. When two cars crash they are exposed to the same force, but the damage severity is different depending on various factors such as car mass, change of speed and design of the car. To explore the relative risks between different car models, we build a model where we let car mass, change of speed and design of the car explain the injury outcome in the crashes. The mathematical model we use is a birth process where we let the states correspond to the injury classes. A data base containing police reported traffic accidents and hospital information is used to explore the relationships in our model.A bootstrap analysis is made to produce a picture of the uncertainty of the estimates. The uncertainty from the bootstrap analysis is compared to the asymptotic estimate of the uncertainty given by the inverse of an information sub-matrix.