%0 Journal Article
%T Network-constrained On-line Path Prediction Based on Global Learning Mechanism<br>受限路网中基于全局学习机制的在线轨迹预测
%A XU Huai-ye
%A DING Zhi-ming
%A LIU Kui-en
%A XU Jia-jie
%A <br>徐怀野
%A 丁治明
%A 刘奎恩
%A 许佳捷
%J 计算机科学
%D 2012
%I 
%X The trajectory prediction of the moving object in the network-constrained has been the hot spot of the intclligent 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 I_PP(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 frectuent 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.
%K Network-constrained
%K Moving object
%K On-line traj ectory prediction
%K Global learning mechanism<br>受限路网
%K 移动对象
%K 在线轨迹预测
%K 全局学习机制
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=FDABA0B07E06BAF88EAE980F7A85D202&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=5D311CA918CA9A03&sid=954CE65414DD94CA&eid=9D453329DCCABB94&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0