In recent years, with the vigorous development of intelligent transportation systems, traffic control and traffic flow guidance have become popular issue of intelligent transportation systems (ITS). The key issue to achieve traffic control guidance is to realize real-time and accurate short-term traffic flow forecasting. And the accuracy and real-time of prediction directly impact traffic control and induced effect. Achieving an accurate prediction of urban road short-term traffic flow is the key of urban road traffic control and traffic guidance. Since the single prediction method for the current is of low precision, we proposed wavelet and support vector machine (SVM) method to predict new fusion; in order to avoid falling into local optimal problem in the process while learning SVM knowledge, we use the particle swarm optimization (PSO) to optimize the key parameters of SVM and in order to improve the prediction accuracy of short-term traffic flow.
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