东南大学学报（自然科学版）2017，Vol.47Issue(5)：1032-1036,5.DOI:10.3969/j.issn.1001-0505.2017.05.030
基于核函数切换和支持向量回归的交通量短时预测模型
Traffic volume prediction based on support vector regression with switch kernel functions
摘要
Abstract
To simulate the nonlinear,probabilistic and complicated patterns in the short-term change of the highway traffic volume,a prediction model was proposed based on support vector regression and switch kernel functions.First,support vector regression models were built with different kernel functions by the historical data and the best kernel function was obtained using the fitting error.Then,a support vector machine model was trained.Finally,the best kernel function for the prediction interval was selected and the corresponding support vector regression model was implemented.A case study was used to evaluate the performance of the proposed model.The result shows that the model is superior to the traditional support vector regression model on the predicted accuracy,and thus it is more robust.关键词
交通运输系统工程/交通量/短时预测/支持向量回归/核函数Key words
system engineering of communication and transportation/traffic volume/short-term prediction/support vector regression/kernel function分类
交通工程引用本文复制引用
李林超,张健,杨帆,冉斌..基于核函数切换和支持向量回归的交通量短时预测模型[J].东南大学学报（自然科学版）,2017,47(5):1032-1036,5.基金项目
交通运输部科技示范工程资助项目(2015364X16030,2014364223150)、国家自然科学基金资助项目(6161001115)、东南大学优秀博士学位论文基金资助项目(YBJJ1736). （2015364X16030,2014364223150）
