Network-constrained On-line Path Prediction Based on Global Learning Mechanism
受限路网中基于全局学习机制的在线轨迹预测

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.