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A probabilistic approach to detect mixed periodic patterns from moving object data

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Abstract

The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects.

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Acknowledgment

This work was supported by the joint fund of the National Natural Science and Civil Aviation research foundation of China (No. U1533114), the State Key Laboratory of Coal Resources and Safe Mining Open Research Project (SKLCRSM14KFB04), U.S. National Science Foundation grants IIS-0905215, CNS-0931975, CCF-0905014 and IIS-1017362. The authors would like to thank anonymous reviewers for their insightful remarks which significantly improve this paper.

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Authors and Affiliations

  1. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, China

    Jun Li

  2. Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing, China

    Jun Li & Qiming Qin

  3. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Jingjing Wang, Tanvi Jindal & Jiawei Han

  4. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China

    Junfei Zhang

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  1. Jun Li
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  2. Jingjing Wang
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  4. Qiming Qin
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  5. Tanvi Jindal
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  6. Jiawei Han
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Correspondence to Qiming Qin.

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Li, J., Wang, J., Zhang, J. et al. A probabilistic approach to detect mixed periodic patterns from moving object data. Geoinformatica 20, 715–739 (2016). https://doi.org/10.1007/s10707-016-0261-2

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  • DOI: https://doi.org/10.1007/s10707-016-0261-2

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