摘要
基于实际交通流变化的不确定性和交通系统时变复杂的特征,应用小波分析理论,对原始交通流数据进行消噪处理,使消噪后的数据更能反映交通流的本质及变化规律。再针对交通流的非线性特征及其短期可预测性,应用混沌时间序列预测模型来预测短时交通量。结果表明:先进行小波消噪再进行预测所得的结果与实测值有更高的拟合度,可以用于短时交通流的预测。
Based on the analysis of the characteristics of nonlinearity and strong interference of traffic flow to the complex and uncertainty of time variance in real traffic system,a new approach was proposed for traffic flow prediction.First,wavelet transform is employed to eliminate the noise of original traffic data to reflect the essence and variation of traffic flow.According to the nonlinearity and predictability in short time of traffic flow,a chaotic time series model was applied to forecast traffic flow with the worked data was proposed.The test result indicates that the model based on wavelet denoising was an efficient method to the real-time dynamic traffic flow prediction.
出处
《科学技术与工程》
2010年第31期7848-7851,7861,共5页
Science Technology and Engineering
基金
国家资金基金项目
(U0970116)资助
关键词
小波消噪
混沌
时间序列
交通流
预测
wavelet de-noising chaos time series traffic flow prediction
