Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper. The selection of parameters greatly affects the prediction accuracy of support vector machine. Introducing particle swarm optimization can find the optimal parameters and improve the prediction accuracy of support vector machine by parameter optimization. However, standard particle swarm optimization is easy to trap into the local optimum, so that the best parameter solutions cannot be found. Therefore, the mutation operation of the genetic algorithm is introduced into particle swarm optimization, particle swarm with mutation optimization is generated. It expands the search space and makes parameter selection more accurate. This paper predicts fatalities of traffic accident using small samples and nonlinear data. The results show that compared with particle swarm with mutation optimization back propagation neural network prediction model, particle swarm optimization-support vector machine model, support vector machine, back propagation neural network, K Nearest Neighbor (K-NN), and Bayesian network, the prediction model of traffic fatalities based on particle swarm with mutation optimization-support vector machine has higher prediction precision and smaller errors. It is feasible and effective to use particle swarm with mutation optimization to optimize the parameters of support vector machine, and this model can predict the accident more accurately