Predicting the number of accidents at a road junction

We consider a model, within a Bayesian framework, which can be used to predict the number of accidents occurring at a road junction in a given period of time. The predictions are based on measurements of the traffic flows as well as on covariates which describe important features of the junctions. Various approximate and estimative methods, which use Gibbs sampling, posterior normality and Laplace approximations, are considered and compared. Procedures to assess the importance of the different covariates through the use of the Kullback-Leibler measure of divergence are also developed.