Based On Traffic Flow And Chaos Theory Of Traffic Flow Chaos Identification And Prediction Research

Traffic flow system is a human-joined, changeable, open and complex huge system. It is high nonlinear and uncertain. Under certain condition, chaos appears in it. The summary of studies on chaos in traffic flow suggests that study on chaos is of great theoretical and practical value. In addition, the summary of methods to identify chaos shows the limitation on current methods and direction and angle of study on identification of chaos in traffic flow. The good frequency aggregation of Wigner-ville distribution and Lebesgue measure is used by this new method to predicating chaos which is in the traffic flow.It can show the chaotic attractor's motion trail without phase space reconstruction. It can also improve operating speed and reduce the sample of traffic flow time series. Through the simulation experiment results indicate that this new methods to identification of chaos in traffic flow can be fit for requirements of veracity. Then, Using based on the BP neural network and chaos theory to predicting the traffic flow. Comparing data with the prediction method of traditional neural network.The result shows that because of reconstructing phase space, The BP&chaos methods is certain to improve the forecast accuracy.At last we drew the conclusion, the BP net forecast method based on the chaos neural network can adapt the accuracy and the timely request than the traditional neural network method in traffic flow forecasting.