Traffic Flow Prediction Based on Wavelet Analysis, Genetic Algorithm and Artificial Neural Network

Based on the analysis of the characteristics of nonlinearity and strong interference of traffic flow due to the complex and uncertainty of time variance in real traffic system, a new approach has been proposed for traffic flow prediction. First, wavelet transform is used to eliminate the noise of original traffic data. Then it decomposes the traffic flow sequence into the low and high frequencies in the multiscale analysis and restores the trend components. The artificial neural network is used in multi-scale forecasting of these coefficients, in which gene algorithm is used to optimize the artificial neural network. Finally, the real detected traffic data are used to testify the precision of the model, and the results show that the model can produce more accurate predictions than that of traditional artificial neural network model.