CAJ | 학술논문

针对断面交通检测数据往往存在着错误、缺失、包含较多噪声等问题，提出了一种基于支持向量回归机的数据预处理方法。先将相邻路段的数据运用线性回归思想筛选、重组，添加到支持向量回归机的数据集中，然后对相邻路段与预测路段之间线性关系进行实时的、动态的分析和计算，从而避免了数据丢失，既有效地压缩了训练集特征数，提高了计算效率，也提高了模型的泛化能力。实验结果表明，对比未作预处理的SVR模型，改进后的模型拟合度提高了25倍，均方误差也明显减小。
침대단면교통검측수거왕왕존재착착오、결실、포함교다조성등문제，제출료일충기우지지향량회귀궤적수거예처리방법。선장상린로단적수거운용선성회귀사상사선、중조，첨가도지지향량회귀궤적수거집중，연후대상린로단여예측로단지간선성관계진행실시적、동태적분석화계산，종이피면료수거주실，기유효지압축료훈련집특정수，제고료계산효솔，야제고료모형적범화능력。실험결과표명，대비미작예처리적SVR모형，개진후적모형의합도제고료25배，균방오차야명현감소。
To solve the traffic data loss and low computational efficiency of many models in the field of prediction of traffic flow,this paper proposed a data preprocessing method based on support vector regression machine.Firstly,it filtered and re-combined the data of adjacent sections based on the idea of linear regression prediction,and then the processed data was added to the data set of support vector regression machine.Secondly,it analyzed and calculated the linear relationship between adja-cent sections and forecast section in real time.Thus avoided the loss of data,and compressed characteristics of training sets ef-fectively,so that improved the computational efficiency and the generalization ability of the model.The experimental results show that the improved model enhances the R-Squared by 25 times comparing with the none-pretreatment model,and mean square error reduces obviously.