基于小波分析的短时交通流非参数回归预测

短时交通流预测是交通诱导与控制的关键技术之一.传统的预测方法难以预测短时状况下具有较强不确定性的交通流.根据交通流信号在不同的时频域空间的不同特性,提出一种组合小波分析和非参数回归的短时交通流预测方法,并对其原理进行了详细分析和描述.首先对交通流时序信号进行多分辨率小波分解,然后对低频和高频分量分别进行单支重构.在此基础上,引入非参数回归对各频率部分分别进行预测,组合各频率空间的预测分量获取预测结果.实验结果验证了该方法的有效性和可行性.

Wavelet analysis-based NPR prediction of short-term traffic flow

Short-term forecasting for traffic flow is one of the key techniques in traffic route guidance and control.It is difficult for classical approaches to effectively forecast the short-term traffic volume with serious uncertainty.According to traffic flow signals showing different characteristics in different scale spaces,a short-term forecasting approach was proposed through combining wavelet analysis with non-parametric estimation.The principle was analyzed and described in detail.Firstly,original traffic flow time series were processed by wavelet multi-resolution decomposition,low frequency and high frequency signals were obtained and reconstructed;secondly,the reconstructed signals were predicted with the non-parametric regression(NPR) model.Finally,the forecasting results by the NPR model are integrated to acquire traffic volume prediction.The experiment results demonstrate that the proposed approach is effective and practical.