中文摘要：

提出一种融合语义特征的移动对象轨迹预测方法．该方法首先将用户的地理位置轨迹转化成语义轨迹，挖掘出语义模式集，同时在语义轨迹中分析用户的移动行为和规律，将具有相似语义行为的用户进行聚类，并挖掘出每个聚类的地理模式集．然后，基于挖掘到的用户个体语义模式集和相似用户地理模式集，构造用来索引和局部匹配的模式树STP-Tree和SLP-Tree．通过对STP-Tree和SLP-Tree的索引和局部匹配，引入一个加权函数实现给定对象运动的语义位置预测．此方法在传统的地理模式预测方法的基础上融合语义特征，可以有效地提取用户的语义活动行为，克服地理位置点特征的局限．在大量真实和人工轨迹数据集上的实验结果表明：该方法的预测准确率较传统方法均有显著提高．

英文摘要：

In this paper, we propose a trajectory prediction approach for mobile objects by combining semantic features. Firstly, the geographic trajectories of all users are transformed to the semantic behaviors trajectories. Then the semantic trajectory pattern sets are extracted. The common behavior of mobile users is analyzed in semantic trajectories and the users are clustered based on the semantic behavior similarity, by which geographic trajectory pattern sets are discovered. Based on the semantic trajectory pattern sets of individual users and the geographic trajectory pattern sets of similar users, the STP-Tree and SLP-Tree are constructed. By indexing and partly matching on the two pattern trees and introducing a weigh function, our method can predict a user＇s recent move position. The proposed method can effectively extract users＇ behaviors and adjust inaccurate prediction results compared with the methods using only geographic features. Experimental results on a large number of real-world and synthetic data sets show that the precision of our method are significantly improved compared with the state-of-the-art methods.