[1]潘 晓,马 昂,郭景峰,等.基于时间序列的轨迹数据相似性度量方法研究及应用综述[J].燕山大学学报,2019,43(6):531-545.[doi:10.3969/j.issn.1007-791X.2019.06.009]
 PAN Xiao,MA Ang,GUO Jingfeng,et al.Research and application survey of similarity measurement methods on trajectory data based on time series[J].Journal of YanShan University,2019,43(6):531-545.[doi:10.3969/j.issn.1007-791X.2019.06.009]
点击复制

基于时间序列的轨迹数据相似性度量方法研究及应用综述
分享到:

《燕山大学学报》[ISSN:1007-791X/CN:13-1219/N]

卷:
43
期数:
2019年第6期
页码:
531-545
栏目:
计算机与控制工程
出版日期:
2019-11-30

文章信息/Info

Title:
Research and application survey of similarity measurement methods on trajectory data based on time series
文章编号:
1007-791X(2019)06-0531-15
作者:
潘 晓1马 昂1郭景峰2*吴 雷12刘风阳3
1.石家庄铁道大学 经济管理学院,石家庄 050043;
2.燕山大学 信息科学与工程学院,河北 秦皇岛 066004;
3.天津师范大学 软件学院,天津 300387
Author(s):
PAN Xiao1 MA Ang1 GUO Jingfeng2 WU Lei12 LIU Fengyang3
1. School of Economic and Management, Shijiazhuang Tiedao University, Shijiazhuang,Hebei 050043, China; 
2. School of Information Science and Engineering, Yanshan University, Qinhuangdao,Hebei 066004, China; 
3. School of Software, Tianjin Normal University, Tianjin, 300387, China
关键词:
相似性度量轨迹数据轨迹计算移动计算
Keywords:
similarity measurement trajectory data trajectory computing mobile computing
分类号:
TP311
DOI:
10.3969/j.issn.1007-791X.2019.06.009
文献标志码:
A
摘要:
移动通信和传感设备等位置感知技术的发展,将人和事物的地理位置数据化。由此形成的轨迹数据正以极快的速度产生并呈指数级增长。轨迹数据中蕴含着丰富的信息,对其分析和挖掘具有重要的社会和应用价值。轨迹相似性度量研究是轨迹数据管理和分析的基础,在轨迹计算中起决定性作用。轨迹相似性度量通常以时间、空间或若干关键词作为参数,度量轨迹之间的相似程度。本文总结归纳了现有的轨迹相似性度量经典研究工作。首先,总结归纳了轨迹大数据的高维异构、多粒度、不确定、高冗余的特点,对轨迹的相似性度量问题进行了形式化的描述。其次,依据不同的数据类型,就经典的空间相似性、文本相似性和时间相似性的评价方法分别进行了说明和总结;依据轨迹形式与度量范围,将现有工作分为了基于离散点的轨迹全局和局部相似性,基于线段的轨迹全局和局部相似性的计算方法,并评价了各种方法的优缺点。再次,分析了轨迹相似性度量在交通管理、城市规划、智能推荐、智慧出行等领域的具体应用。最后,总结展望了轨迹相似性度量在未来研究与应用方面的发展方向。
Abstract:
The development of location-aware technologies such as mobile communications and sensing devices digitizes the geographic location of people and objects. Trajectory data is generating at an extremely fast rate and growing exponentially. Trajectory data contains a wealth of information. It shows great value of the applications based on analyzing and mining trajectory data. Trajectory similarity measurement research is the basis of trajectory data management and analysis, it plays a decisive role in trajectory calculation. To the best of our knowledge, trajectory similarity measure usually takes the temporal, spatial, or textual information as the parameters. The existing classical research works on trajectory similarity are inducted. Firstly, four features of big trajectory data, such as high dimensional heterogeneity, multi-granularity, uncertainty and high redundancy with noise, are summarized and then the trajectory similarity problem is formed. Secondly, the well-known measurements on spatial similarity, text similarity and time similarity are summarized according to different data type. Based on the trajectory form and the measurement range, the existing similarity work can be divided into four categories, i.e. The global and local trajectory similarity on discrete points, global and local similarity of trajectory on segment. Thirdly, various applied scenarios are discussed, including traffic management, urban planning, intelligent recommendation, smart travel, and so on. Finally, the development direction of trajectory similarity measurement on research problem and application scenarios are predicted.

备注/Memo

备注/Memo:
收稿日期:2018-08-16        责任编辑:孙峰
基金项目:国家自然科学基金资助项目(61472340, 61772533);河北省自然科学基金资助项目(F2018210109);河北省教育厅重点项目(ZD2018040);河北省引进留学人员资助项目(C201822);河北省基础研究团队项目;石家庄铁道大学第四届优秀青年科学基金项目(Z661250444)
作者简介:潘晓(1981-),女,河北邢台人,博士,副教授,博士生导师,主要研究方向为数据管理、移动计算、隐私保护;*通信作者:郭景峰(1964-),男,黑龙江齐齐哈尔人,博士,教授,博士生导师,主要研究方向为数据库理论及应用、数据挖掘、社交网络、图像处理,Email:jfguo@ysu.edu.cn
更新日期/Last Update: 2020-01-03