A Cascading Wavelet-Feed Forward Neural Network Approach for Forecasting Traffic Flow

Predicting Tra c flow in the busiest cities has become a popular research area in the past decades. The rapid development of intelligent tra c management system attracts the software industry to come up with e cient tools for tra c prediction over the roads. In this study, Discrete Wavelet Transformation (DWT) is employed with Artificial Neural Network (ANN) to forecast the tra c flow over the roads by analyzing loop sensor’s data. An Information Theoretic Approach has been extended for choosing the number of nodes in hidden layer of Neural Network for the proposed model. The proposed hybrid model was compared with standard Artificial Neural Network (ANN) model. The forecasted results showed that proposed joined Wavelet and Feed Forward Neural Network (WFFNN) worked much well over the experimental data than ANN model