时空轨迹序列模式挖掘方法综述

在全球定位、移动通信技术迅速发展的背景下涌现出了海量的时空轨迹数据，这些数据是对移动对象在时空环境下的移动模式和行为特征的真实写照，蕴含了丰富的信息，这些信息对于城市规划、交通管理、服务推荐、位置预测等领域具有重要的应用价值，而时空轨迹数据在这些领域的应用通常需要通过对时空轨迹数据进行序列模式挖掘才能得以实现。时空轨迹序列模式挖掘旨在从时空轨迹数据集中找出频繁出现的序列模式，例如： 位置模式（频繁轨迹、热点区域）、活动周期模式、语义行为模式，从而挖掘时空数据中隐藏的信息。总结近年来时空轨迹序列模式挖掘的研究进展，先介绍时空轨迹序列的数据特点及应用，再描述时空轨迹模式的挖掘过程：从基于时空轨迹序列来挖掘位置模式、周期模式、语义模式这三个方面来介绍该领域的研究情况，最后阐述现有时空轨迹序列模式挖掘方法存在的问题，并展望时空轨迹序列模式挖掘方法未来的发展趋势。

Review of spatio-temporal trajectory sequence pattern mining methods

With the rapid development of global positioning technology and mobile communication technology, huge amounts of trajectory data appear. These data are true reflections of the moving patterns and behavior characteristics of moving objects in the spatio-temporal environment, and they contain a wealth of information which carries important application values for the fields such as urban planning, traffic management, service recommendation, and location prediction. And the applications of spatio-temporal trajectory data in these fields usually need to be achieved by sequence pattern mining of spatio-temporal trajectory data. Spatio-temporal trajectory sequence pattern mining aims to find frequently occurring sequence patterns from the spatio-temporal trajectory dataset, such as location patterns (frequent trajectories, hot spots), activity periodic patterns, and semantic behavior patterns, so as to mine hidden information in the spatio-temporal data. The research progress of spatial-temporal trajectory sequence pattern mining in recent years was summarized. Firstly, the data characteristics and applications of spatial-temporal trajectory sequence were introduced. Then, the mining process of spatial-temporal trajectory patterns was described:the research situation in this field was introduced from the perspectives of mining location patterns, periodic patterns and semantic patterns based on spatial-temporal trajectory sequence. Finally, the problems existing in the current spatio-temporal trajectory sequence pattern mining methods were elaborated, and the future development trends of spatio-temporal trajectory sequence pattern mining method were prospected.