计算机应用研究2017，Vol.34Issue(1)：91-93,97,4.DOI:10.3969/j.issn.1001-3695.2017.01.018
基于深度学习的短时交通流预测
Short-term traffic flow prediction based on deep learning
摘要
Abstract
In view of the existing prediction methods fail to fully reveal the nature of the traffic flow,this paper proposed a short-term traffic flow prediction model based on deep learning.The method combined the deep belief network (DBN )model and support vector regression(SVR)classifier as predictive model.It removed the trend of the traffic flow by using data diffe-rence,extracted traffic flow features by deep belief network model,carried and the traffic flow prediction out with support vector regression in the top level of the network.Experiment results with actual traffic flow data show that the proposed method has a higher accuracy compared with others,prediction performance increases by 18.01%,and it is an effective traffic flow prediction method.关键词
交通流预测/深度学习/短时交通流/支持向量回归Key words
traffic flow prediction/deep learning/short-term traffic flow/support vector regression分类
信息技术与安全科学引用本文复制引用
罗向龙,焦琴琴,牛力瑶,孙壮文..基于深度学习的短时交通流预测[J].计算机应用研究,2017,34(1):91-93,97,4.基金项目
国家交通运输部重大科技专项项目 （）
