Two Algorithms of Dynamic Travel Time Prediction are Compared on the Basis of the Floating Car

摘要：

Dynamic traffic prediction is important contents of Intelligence Transportation System. The method of building discrete Kalman filter model and the relevant adaptive filter model are proposed by the actual float GPS data, and the accuracy of two models is compared. The result shows that the Kalman filter model is better than the relevant adaptive filter method in predicting accuracy, but with time passed, the error of the relevant adaptive filter isnt accumulated, while the volatility of the Kalman filter become apparent

关键词： Kalman the relevant method the adaptive filtering GPS datum predicting dynamic travel time

作者: Hui-Feng Ji Ai-Gong Xu

作者单位: School of Geomatics Liaoning Technical University, Fuxin,Liaoning, China School of Geomatics Liaoning Technical University, Fuxin, Liaoning, China

会议类型: 国际会议

会议名称: The 8th Asian Symposium on Geographic Information Systems from a Computer Science & Engineering Viewpoint(ASGIS 2010)(第八届亚洲地理信息系统国际学术研讨会)

会议地点: 重庆

会议语种:英文

页码: 176-179

在线出版日期: 2010-04-22（万方平台首次上网日期，不代表论文的发表时间）