This study develops neural network models to explore the nonlinear relationship between
crash frequency by severity and risk factors. To eliminate the possibility of over-fitting and to
deal with black-box characteristic, a network structure optimization and a rule extraction
method are proposed. A case study compares the performance of the modified neural
network models with that of the traditional multivariate Poisson-lognormal model for
predicting crash frequency by severity on road segments in Hong Kong. The results indicate …