基于GA 优化IWNN的短时交通流量预测方法.

Traffic flow prediction is a very important research area of intelligent transportation system, and has a very important academic value and practical significance to improve the traffic congestion problems. Traditional prediction methods which used determined mathematical model would not meet the needs of prediction accuracy and convergence speed during the traffic management control because of nonlinear, complexity and uncertainty of traffic flow. In order to forecast traffic flow accurately, real-timely and efficiently, a new algorithm is proposed by combining wavelet theory and neural network, and constructing an IWNN( improved wavelet neural network) with improved network training methods. At the same time, the initial weights are optimized by GA ( genetic algorithm). It can improve prediction accuracy, speed up the convergence speed and avoid entering local minima. The simulation results show that it can get better prediction results. [ABSTRACT FROM AUTHOR]