受限路网中基于全局学习机制的在线轨迹预测  

Network-constrained On-line Path Prediction Based on Global Learning Mechanism

作　　者：徐怀野[1,2] 丁治明[1] 刘奎恩[1] 许佳捷[1] 

机构地区：[1]中国科学院软件研究所,北京100190 [2]中国科学院研究生院,北京100049

出　　处：《计算机科学》2012年第8期169-172,共4页Computer Science

基　　金：国家自然科学基金项目(60970030);教育部留学回国人员科研启动基金项目(外教司[2006J331])资助

摘　　要：受限路网中移动对象的轨迹预测已成为智能交通关注的热点,被广泛应用于应急保障、车辆导航等领域。但在仅知道移动对象近期轨迹的情况下,现有方法难以解决其未来路径的在线预测问题。提出一种新的在线轨迹预测方法LPP,即通过全局学习机制发现最长频繁路径,构造快速访问结构LPP-tree。基于移动对象近期轨迹可对未来运动路径进行快速在线预测。通过实验,验证了该方法的有效性。The trajectory prediction of the moving object in the network-constrained has been the hot spot of the intelligent traffic’s attention.And it has been widely used in the area of emergency security,GPS and so on.But if we only know the recent trajectory of the moving object,we couldn’t predict its future trajectory with the existing methods.A trajectory prediction’s method LPP（longest frequent path prediction） was put forward,which could construct the fast accessing structure LPP-tree through the global learning mechanism to find out the longest frequent trajectory.Based on the recent trajectory of the moving object,one could predict its future trajectory swiftly online.And the experiment proves the validity of this method.

关 键 词：受限路网 移动对象 在线轨迹预测 全局学习机制 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]