非参数小波算法的交通流预测方法

及时准确地进行交通流短时预测是智能交通系统,尤其是其先进的交通管理系统与先进的出行者信息系统研究的关键内容之一.随着预测时间跨度的缩短,交通流量的变化显示出越来越强的不确定性,使得一般方法的预测精度大大降低.本文应用非参数回归理论并结合小波分析算法,将交通流数据按不同频道分解,然后重构信号时舍去噪音频道,得到光滑的交通信号曲线,进而利用非参数方法中的最近邻规则对未来交通流进行预测.经过实测数据验证,算法能对交通参数做出很好的预测.

Traffic Flow Forecasting Method Based on Wavelet Non-parametric Regression Algorithms

Accurate short-term traffic flow forecasting is becoming a crucial step in ITS research,especially,for its Advanced Traffic Management System and Advanced Traveler Information System research. With the shortening of the forecasting term, the uncertainty of traffic flow becomes more and more seriously, so that the forecasting effect of general approaches is decreasing. Utilizing non-parametric regression theory and wavelet algorithm, the traffic flow data can be decomposed in different channels, and reconstructed before taking out the noise of the traffic flow information. Then, using the nearest neighbor method in non-parametric regression, the data of traffic flow can be forecasted. According to results of the test, the algorithm performs better in short-term traffic flow forecasting.