A stayed location estimation method for sparse GPS positioning information based on positioning accuracy and short-time cluster removal

Sae Iwata, Tomoyuki Nitta, Toshinori Takayama, Masao Yanagisawa, Nozomu Togawa

    研究成果: Article

    2 引用 (Scopus)

    抄録

    Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the e ective radius is set for every cluster based on positioning accuracy and the clusters are merged e ectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.

    元の言語English
    ページ(範囲)831-843
    ページ数13
    ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
    E101A
    発行部数5
    DOI
    出版物ステータスPublished - 2018 5 1

    Fingerprint

    Location Estimation
    Positioning
    Global positioning system
    Mobile devices
    Mobile Devices
    Battery
    Radius
    Interval
    Cell
    Demonstrate
    Experiments
    Experiment

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Graphics and Computer-Aided Design
    • Electrical and Electronic Engineering
    • Applied Mathematics

    これを引用

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    abstract = "Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the e ective radius is set for every cluster based on positioning accuracy and the clusters are merged e ectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33{\%} and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.",
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    AU - Iwata, Sae

    AU - Nitta, Tomoyuki

    AU - Takayama, Toshinori

    AU - Yanagisawa, Masao

    AU - Togawa, Nozomu

    PY - 2018/5/1

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    N2 - Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the e ective radius is set for every cluster based on positioning accuracy and the clusters are merged e ectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.

    AB - Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the e ective radius is set for every cluster based on positioning accuracy and the clusters are merged e ectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.

    KW - Clustering

    KW - E ective radius

    KW - Positioning accuracy

    KW - Sparse GPS positioning

    KW - Stayed location estimation

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