%0 Journal Article
%T Short-term traffic volumes forecasting of road network based on rough set theory and support vector machine<br>基于粗集理论和支持向量机的道路网短时交通流量预测*
%A LI Jian-wu
%A CHEN Sen-f
%A HUANG Kun
%A <br>李建武
%A 陈森发
%A 黄鹍
%J 计算机应用研究
%D 2010
%I 
%X In order to solve prediction problem of traffic volumes,this paper presented a scheme to forecast short-term traffic volumes of a road network.In this scheme,firstly,reconstructed the traffic volumes data of multi-road-cross-sections acquired from a road network in the phase space, and extracted correlative information in the traffic volumes richly,then reduced the input information by using the strong qualitative analysis ability of rough set theory,and removed the noise and redundancy in the samples.On the basis of it, using support vector machine,predicted reduced information.In order to obtain optimum predicting accuracy,used genetic algorithm to optimize prediction parameters.Practical case research shows that the predicted result of this method is famous, and this method has biggish applied potentials in the region of traffic control.
%K road network
%K traffic flow
%K phase reconstruction
%K rough set theory
%K support vector machine
%K prediction model<br>道路网
%K 交通流量
%K 相空间重构
%K 粗集理论
%K 支持向量机
%K 预测模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=A04DD1BE271033E9334EE5C89E891CC0&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=F3090AE9B60B7ED1&sid=9E1C1D195CB98FFA&eid=5A4A99FBBB7FDB1B&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=20