基于多重运动特征的轨迹相似性度量模型

朱进, 胡斌, 邵华

朱进, 胡斌, 邵华. 基于多重运动特征的轨迹相似性度量模型[J]. 武汉大学学报 ( 信息科学版), 2017, 42(12): 1703-1710. DOI: 10.13203/j.whugis20150594
引用本文: 朱进, 胡斌, 邵华. 基于多重运动特征的轨迹相似性度量模型[J]. 武汉大学学报 ( 信息科学版), 2017, 42(12): 1703-1710. DOI: 10.13203/j.whugis20150594
ZHU Jin, HU Bin, SHAO Hua. Trajectory Similarity Measure Based on Multiple Movement Features[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1703-1710. DOI: 10.13203/j.whugis20150594
Citation: ZHU Jin, HU Bin, SHAO Hua. Trajectory Similarity Measure Based on Multiple Movement Features[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1703-1710. DOI: 10.13203/j.whugis20150594

基于多重运动特征的轨迹相似性度量模型

基金项目: 

国家自然科学基金 41571389

国家自然科学基金 41501431

苏州科技大学师资培养科研资助项目 331511203

苏州科技大学科研基金青-项目 341731204

详细信息
    作者简介:

    朱进, 博士, 讲师, 主要从事轨迹数据挖掘研究。540896749@qq.com

    通讯作者:

    胡斌, 博士, 副教授。hb_hubin@126.com

  • 中图分类号: P208

Trajectory Similarity Measure Based on Multiple Movement Features

Funds: 

The National Natural Science Foundation of China 41571389

The National Natural Science Foundation of China 41501431

Teacher Training Research Funding Project of Suzhou University of Science and Technology 331511203

Youth Foundation Project of Suzhou University of Science and Technology 341731204

More Information
    Author Bio:

    ZHU Jin, PhD, lecturer, specializes in trajectory data mining. E-mail: 540896749@qq.com

    Corresponding author:

    HU Bin, PhD, associate professor. E-mail: hb_hubin@126.com

  • 摘要: 针对目前只能对单一运动特征(速度、方向等)进行轨迹相似性分析的不足,提出了基于多重运动特征的轨迹相似性度量,该度量对于分析和理解移动对象的运动行为和规律具有重要意义。将其应用于基于多重运动特征的运动序列模式发现。该相似性度量借鉴数据立方体的思想,将多重运动特征时间序列进行量化和符号化表示,在多重运动特征值域空间中计算两字符间的距离作为字符间替换代价,最终以加权编辑距离作为相似性度量。将该相似性度量与谱聚类方法相结合进行运动序列模式发现。实验以飓风数据为例,通过气象文献中飓风的发生与运动规律验证了模型的有效性。
    Abstract: For the shortcoming that existing methods can only measure the trajectory similarity of single movement feature (e.g. velocity, acceleration), the trajectory similarity measure based on multiple movement features is proposed. The measure is significant for analyzing and understanding the movement behaviors and mechanisms of moving objects. The measure borrows the idea of data cube, quantizes and symbolizes the multiple movement feature time series. In multiple movement feature domain space, the Euclidean distances between characters are computed as the substitution costs of weighted edit distance which is computed as the similarity measure. The measure is integrated with the spectral clustering method for movement sequential pattern discovery. Using the hurricane dataset, the known hurricane originating and movement laws in meteorological literatures verify the effectiveness of the measure.
  • 图  1   基于多重运动特征的轨迹相似性度量

    Figure  1.   Trajectory Similarity Measure Based on Multiple Movement Features

    图  2   双重运动特征的量化及符号化

    Figure  2.   The Quantification and Symbolization of Duplex Movement Features

    图  3   三重运动特征速度-方向-曲率的量化及符号化

    Figure  3.   The Quantification and Symbolization of Triple Movement Features Velocity-Direction-Sinuosity

    图  4   速度-方向运动特征聚类分析的聚簇

    Figure  4.   The Clusters of Velocity-Direction Clustering Analysis

    图  5   聚簇的速度特征序列模式

    Figure  5.   The Velocity Sequential Pattern of Clusters

    图  6   聚簇的方向特征序列模式

    Figure  6.   The Direction Sequential Pattern of Clusters

    图  7   速度-加速度-方向运动特征聚类分析的聚簇

    Figure  7.   The Clusters of Velocity-Acceleration-Direction Clustering Analysis

    图  8   聚簇的速度特征序列模式

    Figure  8.   The Velocity Sequential Pattern of Clusters

    图  9   聚簇的加速度特征序列模式

    Figure  9.   The Acceleration Sequential Pattern of Clusters

    表  1   利用曼-惠特尼U检验得到的p

    Table  1   The p Value of Mann-Whitney U Test

    运动特征特征起源点纬度起源点经度低纬/高纬起源点在80°W东或西季节(夏/秋)时间粒度单位
    v-d 0.000 0.000 0.000 0.000 0.008 0.006
    双重 v-ta 0.000 0.000 0.000 0.0000.3110.468
    v-s 0.000 0.000 0.000 0.000 0.0140.059
    三重 v-a-d 0.000 0.000 0.000 0.000 0.027 0.008
    v-ta-d 0.000 0.000 0.000 0.000 0.001 0.003
    v 0.0160.6110.3100.5570.0940.124
    单一 d 0.000 0.000 0.000 0.0050.3540.126
    ta0.5450.1010.5760.8340.6800.500
    注:p值小于0.05用粗体表示
    下载: 导出CSV
  • [1]

    Elsner J B, Kara A B. Hurricanes of the North Atlantic:Climate and Society[M]. New York:Oxford University Press, 1999:21-24

    [2]

    Zheng Y, Liu L, Wang L, et al. Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web[C].The 17th International Conference on World Wide Web (WWW'08), Beijing, China, 2008

    [3]

    Zheng Y, Li Q, Chen Y, et al. Understanding Mobility Based on GPS Data[C].The 10th International Conference on Ubiquitous Computing (UbiComp'08), Seoul, Korea, 2008

    [4] 张治华. 基于GPS轨迹的出行信息提取研究[D]. 上海: 华东师范大学, 2010

    Zhang Zhihua. Deriving Trip Information from GPS Trajeetories[D]. Shanghai:East China Normal University, 2010

    [5]

    Chen J, Shaw S L, Yu H, et al. Exploratory Data Analysis of Activity Diary Data a Space-Time GIS Approach[J]. Journal of Transport Geography, 2011, 19(3):394-404 doi: 10.1016/j.jtrangeo.2010.11.002

    [6]

    Dodge S, Weibel R, Laube P. Trajectory Similarity Analysis in Movement Parameter Space[C]. GISRUK, UK, 2011

    [7]

    Dodge S, Laube P, Weibel R. Movement Similarity Assessment Using Symbolic Representation of Trajectories[J]. International Journal of Geographical Information Science, 2012, 26(9):1563-1588 doi: 10.1080/13658816.2011.630003

    [8]

    Laube P, Dennis T, Forer P, et al. Movement Beyond the Snapshot -Dynamic Analysis of Geospatial Lifelines[J]. Computers, Environment and Urban Systems, 2007, 31(5):481-501 doi: 10.1016/j.compenvurbsys.2007.08.002

    [9] 李静伟. 基于共享近邻的自适应谱聚类算法[D]. 大连: 大连理工大学, 2010

    Li Jingwei. Adaptive Spectral Clustering Based on Shared Nearest Neighbors[D]. Dalian:Dalian University of Technology, 2010

    [10]

    Han J, Kamber M, Pei J. Data Mining Concepts and Techniques[M]. 3rd Edition. Waltham:Elsevier, 2012

    [11]

    Mardia K V, Jupp P E. Directional Statistics[M]. Chichester UK:John Wiley & Sons, 2000:13-23

    [12]

    Levenshtein V I. Binary Codes Capable of Correcting Deletions, Insertions, and Reversals[J].Soviet Physics Doklady, 1966, 10(8):707-710

    [13]

    Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to Algorithms[M]. 3rd Edition. Cambridge:MIT Press, 2009

    [14]

    Li Y, Liu B. A Normalized Levenshtein Distance Metric[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):1091-1095 doi: 10.1109/TPAMI.2007.1078

    [15] 蔡晓妍, 戴冠中, 杨黎斌.谱聚类算法综述[J].计算机科学, 2008, 35(7):14-18 doi: 10.3969/j.issn.1002-137X.2008.07.004

    Cai Xaoyan, Dai Guanzhong, Yang Libin. Survey on Spectral Clustering Algorithms[J]. Computer Science, 2008, 35(7):14-18 doi: 10.3969/j.issn.1002-137X.2008.07.004

    [16]

    Zelnik-Manor L, Perona P. Self-Tuning Spectral Clustering[J].Advances in Neural Information Processing Systems, 2004:1601-1608 http://lihi.eew.technion.ac.il/files/Demos/SelfTuningClustering.html

图(9)  /  表(1)
计量
  • 文章访问数:  2286
  • HTML全文浏览量:  240
  • PDF下载量:  490
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-04-10
  • 发布日期:  2017-12-04

目录

    /

    返回文章
    返回