Peak hour traffic volume prediction models using a combination method

Accurate traffic forecasts has important role in providing real-time data traffic, reduce congestion on the roads and improve traffic safety. In this study, a combination of multi-layered back propagation neural networks to predict the peak hour traffic flow with wavelet transform is used. According to the proposed method, using the wavelet transform, a pre-processing is done on the data volume traffic flows to obtain more detailed information about the dynamics of the problem; then as intelligence, training and testing data is presented to the neural network. Trained network using validated assessment functions and to predict the peak hour traffic volume may be used during the next week. Predictions made to measure, traditional back propagation neural network design and compared the results with the proposed method. The results show that the proposed method, the peak hour traffic flow with higher precision than conventional neural network predicts. The method proposed in this study has been done on the basis of parameters and can be used in practical applications.