A Survey on Trajectory Data Mining: Techniques and Applications

被引:154
|
作者
Feng, Zhenni [1 ]
Zhu, Yanmin [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, Shanghai Key Lab Scalable Comp & Syst, Shanghai 200240, Peoples R China
来源
IEEE ACCESS | 2016年 / 4卷
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Trajectory data mining; big data applications; data mining techniques; GATHERING PATTERNS; SYSTEM; SEARCH; COMPRESSION; UNCERTAINTY; PREDICTION; DISCOVERY;
D O I
10.1109/ACCESS.2016.2553681
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.
引用
收藏
页码:2056 / 2067
页数:12
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