Spatial Temporal Trajectory Similarity Join

  • Tangpeng Dan
  • Changyin LuoEmail author
  • Yanhong Li
  • Bolong Zheng
  • Guohui Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)


Existing works only focus on spatial dimension without the consideration of combining spatial and temporal dimensions together when processing trajectory similarity join queries, to address this problem, this paper proposes a novel two-level grid index which takes both spatial and temporal information into account when processing spatial-temporal trajectory similarity join. A new similarity function MOGS is developed to measure the similarity in an efficient manner when our candidate trajectories have high coverage rate CR. Extensive experiments are conducted to verify the efficiency of our solution.


Spatial-temporal database Two-level grid index Trajectory similarity join 



This work is supported in part by Hubei Natural Science Foundation under Grant No. 2017CFB135, NSFC Grant No. 61309002, and the Fundamental Research Funds for the Central Universities under Grants No. CCNU18QN017, CZZ17003, and Teaching Research Projects NO. JYX17032.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tangpeng Dan
    • 1
    • 2
  • Changyin Luo
    • 1
    • 2
    Email author
  • Yanhong Li
    • 3
  • Bolong Zheng
    • 4
  • Guohui Li
    • 4
  1. 1.School of ComputerCentral China Normal UniversityWuhanChina
  2. 2.Hubei Provincial Key Laboratory of Artificial Intelligence and Smart LearningCentral China Normal UniversityWuhanChina
  3. 3.College of Computer ScienceSouth-Central University for NationalitiesWuhanChina
  4. 4.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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