基于主成分分析与支持向量机结合的交通流预测

为提高交通流预测的预测精度和预测速度，提出了用非线性回归支持向量机与主成分分析相结合进行交通流预测的方法。主成分分析用来对交通流预测的预测变量进行特征抽取，用较少的主成分代替原预测变量．将生成的主成分输入到非线性回归支持向量机，进行交通流预测，支持向量机的核参数利用Bayesian推理进行确定。通过对济南市交通数据的实例分析来验证该方法的有效性。结果表明，非线性回归支持向量机与主成分分析相结合进行交通流预测不但可以提高交通流预测的精度，同时还可以降低预测所需的计算量，满足交通流预测的实时性要求，预测精度比目前常用交通流预测方法的预测精度有所提高。

Traffic Flow Forecast Based on Combining Principal Component Analysis with Support Vector Machine

For improving traffic flow forecast precision,a forecasting method that combines nonlinear regression support vector machine(SVM) with principal component analysis(PCA) was proposed.PCA was used to extract features from forecasting variables and form fewer principal components.These principal components were input to nonlinear regress SVM for traffic flow forecast.The kernel parameters of SVM were determined with Bayesian inference.The efficiency of the method was illustrated through analyzing the practical...