Traffic flow forecasting research based on Bayesian normalized Elman neural network

In this thesis, a single, separate section, for example, is used to forecast the traffic flow in a long time. The advantage of artificial neural network is its ability of learning or training in other words. By learning, the network can give appropriate output when accepting input. Thus, artificial neural network is a good model for predicting transportation flow. This paper proposes the Bayesian normalized Elman neural network as the prediction model which has the reliability and stability of Elman neural network and is able to overcome the influence of the hidden layer nodes on the prediction accuracy, which improves the generalization ability of the network. Then depending on long-time traffic forecasting results of different neural networks like classical BP, wavelet neural network, statistics accuracy error and comparative analysis are finished to draw a conclusion that combined with Bayesian normalized method based on Elman neural network is more suitable for long time traffic forecast.