A stable and optimized neural network model for crash injury severity prediction

The study proposes a convex combination (CC) algorithm to fast and stably train a neural network (NN) model for crash injury severity prediction, and a modified NN pruning for function approximation (N2PFA) algorithm to optimize the network structure. To demonstrate the proposed approaches and to compare them with the NN trained by traditional back-propagation (BP) algorithm and an ordered logit (OL) model, a two-vehicle crash dataset in 2006 provided by the Florida Department of Highway Safety and Motor Vehicles (DHSMV) was employed. According to the results, the CC algorithm outperforms the BP algorithm both in convergence ability and training speed. Compared with a fully connected NN, the optimized NN contains much less network nodes and achieves comparable classification accuracy. Both of them have better fitting and predicting performance than the OL model, which again demonstrates the NN's superiority over statistical models for predicting crash injury severity. The pruned input nodes also justify the ability of the structure optimization method for identifying the factors irrelevant to crash-injury outcomes. A sensitivity analysis of the optimized NN is further conducted to determine the explanatory variables' impact on each injury severity outcome. While most of the results conform to the coefficient estimation in the OL model and previous studies, some variables are found to have non-linear relationships with injury severity, which further verifies the strength of the proposed method.