Research On Prediction Of Short-time Urban Traffic Flow Based On Wavelet Analysis And Genetic Neural Network

With the economic growth and urbanization progress of our country, Traffic congestion, frequent accidents, exhaust pollution and traffic problems have become the focus of widespread concern in today’s society. Real-time and accurate short-term traffic flow forecasting can provide data support for traffic guidance and control, and it is the base and key to solve many of traffic problems. This paper first analyses and summarizes of the short-term traffic flow prediction, on this basis, do some research on denoising and prediction of short-time traffic data.This paper first analyses the characteristics of the basic concepts of traffic flow forecasting and traffic flow, and establishs performance indicators for short-term traffic flow prediction. In order to improve the accuracy and precision of the collected traffic data, uses the Lagrange interpolation theorem on the error data and missing data interpolation. According to the characteristics of several mother wavelet, select the db5wavelet decomposes3layer on the traffic flow data and denoising. The data were normalizes in order to improve the prediction accuracy and speed of convergence of the algorithm before making the forecast. Considering BP neural network and genetic algorithm defects in the application, this paper uses genetic algorithm to optimizes the BP neural network and then established genetic neural network prediction model. In predicting the simulation phase, this paper uses BP neural network and genetic neural network to predict the raw data and the wavelet denoising data, and calculates the performance indicators for the prediction results of each group. Through comparative and analysis, this paper comes to a conclusion that the denoising data predicted by the genetic neural network have a higher goodness of fit and small error, and the approach is theoretically possible and can improve the accuracy of short-time traffic flow prediction.