Application of Multi-scale Wavelet Kernel in Traffic Flow Forecasting

Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems (ITS). Based on statistical learning theory, support vector machine (SVM) has better generalization performance and can guarantee global minima for given training data. However, the good generalization performance of SVM highly depends on the construction of kernel function. An effective multi-scale Marr wavelet kernel which we combine the wavelet theory with SVM is presented in this paper. The forecasting performance is evaluated by real-time traffic flow data of highway in Los Angeles, USA and a variety of experiments are carried out. Compared to wavelet kernel function and RBF kernel function, the multi-scale wavelet kernel function has much more precise forecasting rate and higher efficiency, especially for boundary approximation.