A New Intelligent Model for Short Time Traffic Flow Prediction via EMD and PSO–SVM

Abstract

Accurate and reliable short time traffic flow forecasting is one of the most important issues in the traffic information management. Due to the nonlinear and stochastic of the data, it is often difficult to predict the traffic flow precisely. Hence, a new hybrid intelligent forecasting approach based on the integration of empirical mode decomposition (EMD), particle swarm optimization (PSO) and support vector machine (SVM) is proposed for the short time traffic flow prediction in this paper. The advantages of the proposed method are that the combination of EMD and PSO–SVM can deal with the nonlinear and stochastic characteristics of the original data well. The forecasting rate may be enhanced using this new technique. Seven-hundred and twenty samples of the practical traffic flow data were applied for the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and improve the prediction accuracy by 10% or better when compared to SVM approach. Thus, the new EMD–PSO–SVM traffic flow forecasting model provides practical application.