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Multiscale permutation mutual information quantify the information interaction for traffic time series

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Abstract

The purpose of this study was to introduce a method in extracting and quantifying the information flow in complex system, which takes into account the temporal structure of the time series at multiple scales. It is important that the method should be able to reflect the intrinsic mechanism of information interaction faithfully. The proposed multiscale permutation mutual information (MPMI) method studies the mutual information based on permutation pattern and multiscale concept from multiscale sample entropy and is initially tested on artificially generated signals for proof of concept by comparing the MPMI results of the iterative amplitude adjusted Fourier transform surrogates and the original series. It is subsequently applied to quantify the information interaction of traffic time series. MPMI results can detect the relationship between neighboring detectors and the effect of traffic accidents on information interaction between speed and volume. MPMI method uncovers the information interaction and provides valuable insight into the underlying mechanisms in traffic system.

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Acknowledgements

The financial support from National Key Technologies R&D Program (2017YFB131201303-08) and National Natural Science Foundation of China (11790281) are gratefully acknowledged.

Funding

This study is funded by National Key Technologies R&D Program (2017YFB131201303-08) and National Natural Science Foundation of China (11790281).

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Yi Yin, Xi Wang & Qiang Li

  2. School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Pengjian Shang

  3. Department of Sleep Medicine, the PLA Air Force Medical Center, Beijing, 100142, People’s Republic of China

    He Gao

  4. Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA

    Yan Ma

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  1. Yi Yin
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  2. Xi Wang
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  4. Pengjian Shang
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  5. He Gao
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  6. Yan Ma
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Correspondence to Yi Yin.

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Yin, Y., Wang, X., Li, Q. et al. Multiscale permutation mutual information quantify the information interaction for traffic time series. Nonlinear Dyn 102, 1909–1923 (2020). https://doi.org/10.1007/s11071-020-05981-0

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  • DOI: https://doi.org/10.1007/s11071-020-05981-0

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