Traffic safety forecasting method by particle swarm optimization and support vector machine

It is important to establish the decision of traffic safety planning by forecasting the development tendency of traffic accident according to the related data of traffic safety in former years. In order to solve the drawbacks of BP neural network, a novel approach which combines particle swarm optimization and support vector machine (PSO–SVM) is presented to traffic safety forecasting. Firstly, influencing factors of traffic safety and evaluation indexes are analyzed, then traffic safety forecasting model by PSO–SVM is established according to the influencing factors. Finally, the data about traffic safety in China from 1970 to 2006 are applied to research the forecasting ability of the proposed method. The experimental results show that traffic safety forecasting by PSO–SVM is better than that by BP neural network.