摘要
时空轨迹(Trajectory)是移动对象的位置和时间的记录序列。作为一种重要的时空对象数据类型和信息源,时空轨迹的应用范围涵盖了人类行为、交通物流、应急疏散管理、动物习性和市场营销等诸多方面。通过对各种时空轨迹数据进行聚类分析,可以提取时空轨迹数据中的相似性与异常特征,并有助于发现其中有意义的模式。本文根据时空轨迹数据的特点,系统综述了时空轨迹聚类方法的研究进展。首先,从理论、可行性和应用的角度分析了时空轨迹数据及其聚类方法研究的重要性,并论述了时空轨迹的定义、模型与表达;然后,按照相似性度量所涉及的不同时间区间将现有的时空轨迹聚类方法划分为6类,并对每一类方法的原理及特点进行了评述;最后,讨论了现有方法面临的主要问题和挑战,并对时空轨迹聚类研究的发展进行了展望。
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics, such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior, and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analy- sis needs to be used to find the implicit patterns in it. Based on the characteristics and the similarity measurements of trajectory data, this paper reviewed the research progresses in trajectory clustering methods. Firstly, the significance of research on trajectory data and its clustering methods was presented. Then the definition, models as well as several visualization methods of trajectories were summarized. After that, the authors classified the ex- isting trajectory clustering methods into 6 main categories according to the similarity measurement of them, and analyzed each of the trajectory clustering methods, along with their respective pros and cons by category. Finally, some research challenges and future directions were discussed.
出处
《地理科学进展》
CSCD
北大核心
2011年第5期522-534,共13页
Progress in Geography
基金
中国科学院知识创新工程重要方向项目(KZCX2-YW-QN303)
中国科学院地理资源所自主部署创新项目(200905004)
863项目(2009AA12Z227)
关键词
时空轨迹
时空数据挖掘
聚类
相似性度量
研究进展
trajectory
spatio-temporal data mining
clustering
similarity measurement
research progress
