吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 913-920.doi: 10.13229/j.cnki.jdxbgxb201503033

Previous Articles     Next Articles

Discovering spatiotemporal hot spot region and mining patterns fro moving trajectory random sampling

WANG Liang1, 2, 3, HU Kun-yuan1, KU Tao1, WU Jun-wei1, 2   

  1. 1.Shenyang Institute of Automation,Chinese Academy of Sciences, Shenyang 110016,China;
    2.University of Chinese Academy of Sciences, Beijing 100039,China;
    3.School of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi'an University of Science and Technology,Xi'an 710054,China
  • Received:2013-08-30 Online:2015-05-01 Published:2015-05-01

PDF (PC)

406

Abstract: The moving trajectory by random sampling distributes unevenly in time dimension. After projecting the three-dimensional spatiotemporal trajectory data into one-dimensional time domain, a spatiotemporal hot spot region discovery and moving pattern mining methods are proposed based on automatic detection of intensive time intervals. Through detecting intensive time intervals dynamically with a bottom-up clustering strategy, the spatiotemporal hot spot regions are discovered in corresponding time intervals. A depth-first algorithm is designed to mine the set of frequency moving patterns. Finally, based on synthetic moving trajectory dataset, the effectiveness and scalability of the proposed algorithms are verified.

Key words: artificial intelligence, data mining, moving trajectory by random sampling, intensive time interval, hot spot region

CLC Number: 

  • TP18
[1] Lee J W, Paek O H, Ryu K H. Temporal moving pattern mining for location-based service[J]. Journal of Systems and Software,2004,73(3):481-490.
[2] 郑宇,谢幸. 基于用户轨迹挖掘的智能位置服务[J]. 中国计算机学会通讯,2010,6(6):23-30.
Zheng Yu,Xie Xing. Enable smart location-based services by mining user trajectories[J]. Communications of the CCF,2010,6(6):23-30.
[3] Cao H P, Mamoulis N, Cheung D W. Mining frequent spatio-temporal sequential patterns[C]∥Fifth IEEE International Conference on Data Mining,2005:82-89.
[4] Lee J G, Han J W, Whang K Y. Trajectory clustering: a partition-and-group framework[C]∥Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data,Beijing,China,2007:593-604.
[5] De Lucca Siqueira F, Bogorny V. Discovering chasing behavior in moving object trajectories[J]. Transactions in GIS,2011,15(5):667-688.
[6] Yanagisawa Y, Akahani J, Satoh T. Shape-based similarity query for trajectory of mobile objects[C]∥Mobile Data Management,Berlin,Germany,2003:63-77.
[7] Giannotti F, Nanni M, Pinelli F, et al. Trajectory pattern mining[C]∥Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,New York,2007:330-339.
[8] Kang J, Yong H S. Mining spatio-temporal patterns in trajectory data[J]. JIPS,2010,6(4):521-536.
[9] Wang L,Hu K,Ku T,et al. Discovering closed frequent patterns in moving trajectory database[C]∥14th International Conference on Communication Technology,Chengdu,2012:567-572.
[10] Lee A J T, Chen Y A, Ip W C. Mining frequent trajectory patterns in spatial—temporal databases[J]. Information Sciences,2009,179(13):2218-2231.
[11] Zheng Y, Lou Y, Zhang C,et al. Map-matching for low-sampling-rate GPS Trajectories[P]. U.S. Patent:12/712,857,2010-02-25.
[12] Pfoser D, Jensen C S. Capturing the uncertainty of moving-object representations[C]∥Advances in Spatial Databases,Berlin,Germany,1999:111-131.
[13] Miwa T, Kiuchi D, Yamamoto T,et al. Development of map matching algorithm for low frequency probe data[J]. Transportation Research Part C: Emerging Technologies,2012,22:132-145.
[14] Theodoridis Y,Nascimento M A. Generating spatiotemporal datasets on the WWW[J]. ACM SIGMOD Record,2000,29(3):39-43.
[15] Theodoridis Y,Silva J R O,Nascimento M A. On the generation of spatiotemporal datasets[C]∥In Proceedings of the 6th Int'l Symposium on Large Spatial Databases,Hong Kong,China,1999:147-164.
[1] DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[2] GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[3] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] DENG Jian-xun, XIONG Zhong-yang, DENG Xin. Improved DNALA algorithm based on spectral clustering matrix [J]. 吉林大学学报(工学版), 2018, 48(3): 903-908.
[9] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[10] YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[11] LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12] QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[13] LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[14] SHAO Ke-yong, CHEN Feng, WANG Ting-ting, WANG Ji-chi, ZHOU Li-peng. Full state based adaptive control of fractional order chaotic system without equilibrium point [J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[15] WANG Sheng-sheng, WANG Chuang-feng, GU Fang-ming. Spatio-temporal reasoning for OPRA direction relation network [J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd