An Online Method for Trajectory Simplification Under Uncertainty of GPS

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Abstract

A novel simplification method for GPS trajectory is presented in this paper. Trajectory simplification can greatly improve the efficiency of data analysis (e.g., querying, clustering). Based on the observation of information content contained by sampling data, we assume that (1) the sampling points on the boundary of MBR (Minimum Bounding Rectangle) contain more information content, (2) the bigger the area of MBR is, the more the points should be stored. We applied these two assumptions in our method to simplify trajectory online. Two main components of this method (i.e., divide/merge principle and selection strategy), are elaborated in the paper. Moreover, we define a new error metric ― enclosed area metric ― to evaluate the accuracy of simplified trajectories, which is proven more robust against the uncertainty of GPS. To implement this measure, we devise a practical algorithm of area calculation for self-intersecting polygons. Through comparing with other methods in a series of experiments over huge dataset, our method is proven effective and efficient.A novel simplification method for GPS trajectory is presented in this paper. Trajectory simplification can greatly improve the efficiency of data analysis (e.g., querying, clustering). Based on the observation of information content contained by sampling data, we assume that (1) the sampling points on the boundary of MBR (Minimum Bounding Rectangle) contain more information content, (2) the bigger the area of MBR is, the more the points should be stored. We applied these two assumptions in our method to simplify trajectory online. Two main components of this method (i.e., divide/merge principle and selection strategy), are elaborated in the paper. Moreover, we define a new error metric ― enclosed area metric ― to evaluate the accuracy of simplified trajectories, which is proven more robust against the uncertainty of GPS. To implement this measure, we devise a practical algorithm of area calculation for self-intersecting polygons. Through comparing with other methods in a series of experiments over huge dataset, our method is proven effective and efficient.

Journal

  • 情報処理学会論文誌データベース(TOD)

    情報処理学会論文誌データベース(TOD) 6(3), 40-49, 2013-06-28

Codes

  • NII Article ID (NAID)
    110009579664
  • NII NACSIS-CAT ID (NCID)
    AA11464847
  • Text Lang
    ENG
  • Article Type
    Article
  • ISSN
    1882-7799
  • Data Source
    NII-ELS  IPSJ 
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