Prediction for Short-Term Traffic Flow Based on Optimized Wavelet Neural Network Model

Short term traffic forecasting has been a very important consideration in many areas of transportation research for more than 3 decades. Short-term traffic forecasting based on data driven methods is one of the most dynamic and developing research arenas with enormous published literature. In order to improve forecasting model accuracy of wavelet neural network, an adaptive particle swarm optimization algorithm based on cloud theory was proposed, not only to help improve search performance, but also speed up individual optimizing ability. And the inertia weight adaptively changes depending on X-conditional cloud generator which has the stable tendency and randomness property .Then the adaptive particle swarm optimization algorithm based on cloud theory was used to optimize the weights and thresholds of wavelet BP neural network, Instead of traditional gradient descent method . At last, wavelet BP neural network was trained to search for the optimal solution. Based on above theory, an improved wavelet neural network model based on modified particle swarm optimization algorithm was proposed and the availability of the modified prediction method was proved by predicting the time series of real traffic flow. At last, the computer simulations have shown that the nonlinear fitting and accuracy of the modified prediction methods are better than other prediction methods.