Paratactic Spatial-temporal Two Dimension Data Fusion Based on Support Vector Machines for Traffic flow Prediction of Abnormal State

摘要：

　　This paper presents a paratactic spatial-temporal 2dimension data fusion model based on support vector machines (SVM) for traffic volume prediction of the abnormal state.Time and space SVM operates respectively in two parallel operating system models to reduce the time cost.By comparing the prediction results with which obtained by the multiple regression prediction method,the prediction accuracy is greatly improved by utilizing the paratactic spatial-temporal dimension data fusion model.Especially in the abnormal state caused by unexpected events (such as:traffic accidents,traffic jam etc),the proposed method can also significantly avoid structural system error of one-dimensional time source data fusion.

关键词： traffic flow prediction support vector machines spatial-temporal 2Dimension data fusion abnormal state

作者: Chen Liang Li Qiaoru Tian Xiaoyong Chen Xiangshang Wang Rongxia

作者单位: School of Civil Engineering, Hebei University of Technology, Tianjin, 300401China;Civil Engineering Technology Research Center of Hebei Province, Tianjin, 300401 China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1225-1229

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）