Traffic flow prediction based on wavelet transform and Radial Basis Function network

Exact prediction of traffic flow is the key technology of traffic flow guidance and traffic system management. A kind of wavelet neural network model combined with the advantages of wavelet transform and RBF network was presented for short-term traffic flow prediction. After the wavelet decomposition and reconstruction were made to traffic flow data with similar periods, signal components respectively were predicted by RBF neural network, and prediction results were synthesized. Furthermore, different time intervals were adopted for prediction and effects were compared with each other according to several evaluation indexes. The results show that prediction effect is better than just predicting by neural network in prediction precision and network convergence. Therefore, there are favorable prospects for applications.