Data Fusion Model Based on Support Vector Machine for Traffic Flow Prediction

摘要：

It is significant for intelligent traffic system management and control how to get accurate prediction of dynamic traffic flow. In this paper, a traffic flow prediction model of spatio-temporal 2D (2-dimension) data fusing based on SVM (Support Vector Machines) is put forward to. Temporal SVM and spatial SVM are running on two paratactic computer systems,and this will considerably save process time cost. The section flow results predicted by temporal SVM, spatial SVM and spatio -temporal 2D data fusing are all satisfied the precision requirement. However, the prediction precise is significantly improved by spatio-temporal 2D data fusing. Especially,when an abrupt incident happens (e.g., jam,traffic accident), system error of temporal prediction be avoided to a great extent with the spatio-temporal 2D data fusing model.

关键词： Traffic control and management real-time flow prediction support vector machine paratactic system spatio-temporal 2Ddata fusing

作者: Liang Chen Weizheng Liu Qiaoru Li Lianyu Wei Shoufeng Ma

作者单位: Civil Engineering School Hebei University of Technology Tianjin, P.R.China Management Department Tianjin University Tianjin, P.R.China.

会议类型: 国际会议

会议名称: 2007 Conference on Systems Science, Management Science and System Dynamics(第二届系统科学、管理科学与系统动力学国际会议)

会议地点: 上海

会议语种:英文

页码: 211-218

在线出版日期: 2007-10-19（万方平台首次上网日期，不代表论文的发表时间）