小波神经网络在短时交通流量预测中的应用

Application of Wavelet Neural Network in Prediction of Short Time Traffic Flow Prediction

研究在短时交通流量预测问题,短时交通流量存在随机性和非线性因素,影响预测的准确性。传统预测模型难以反映交通流量变化特点,同时传统神经网络易陷入局部极小值,泛化能力差,交通流量预测精度低。为了提高短时交通流量预测精度,提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析,并进行非线性预测,验证了模型的有效性,进行了对比试验。验证结果表明,小波神经网络提高了短时交通流量预精度,预测结果更具应用价值。

In order to improve the short-term traffic flow prediction precision, this paper proposed a short-term traffic flow forecasting model based on wavelet neural network. Wavelet neural network can describe the short-term traffic flow＇ s stochastic and uncertainty and predict nonlinearly. The experiment was carried out to verify the validity of the model. The results show that the wavelet neural network improves the traffic flow prediction precision and has good application value.