融合语义特征的移动对象轨迹预测方法  

A Trajectory Prediction Approach for Mobile Objects by Combining Semantic Features

作　　者：黄健斌[1,2] 张盼盼[1] 皇甫学军 孙鹤立[3] 

机构地区：[1]西安电子科技大学软件学院,西安710071 [2]计算机软件新技术国家重点实验室(南京大学),南京210023 [3]西安交通大学计算机科学与技术系,西安710049

出　　处：《计算机研究与发展》2014年第1期76-87,共12页Journal of Computer Research and Development

基　　金：国家自然科学基金项目（61173093,61202182）;陕西省自然科学基金项目（2013JM8019）;中央高校基本科研业务费专项基金项目（K5051323001,2012jdhz07）;中国博士后科学基金面上项目（2012M521776）

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．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.

关 键 词：轨迹预测 模式挖掘 语义特征 移动对象 模式树 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]