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Large deviations estimates for the multiscale analysis of traffic speed time series

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  • Shi, Wenbin
  • Shang, Pengjian
  • Wang, Jing

Abstract

Multifractal behavior is found in traffic speed time series and mostly measured around the concept of Legendre singularity spectrum. As one of the multifractal spectra, which is the probability distribution of roughness grain exponent, Legendre spectrum is structurally blind to subtle features like non-concavity or, to a certain extent non scaling of the distributions. In this article, we illustrate the large deviations spectrum on both artificial and traffic speed time series, and verify that this kind of approach is able to reveal significant information (represents some traffic characteristics here) that remains hidden with Legendre spectrum. In the mean time, the multiscale analysis of the large deviations spectrum was conducted to quantify the presence or absence of scale invariant phenomenon in the study of traffic speed signals.

Suggested Citation

  • Shi, Wenbin & Shang, Pengjian & Wang, Jing, 2015. "Large deviations estimates for the multiscale analysis of traffic speed time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 562-570.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:562-570
    DOI: 10.1016/j.physa.2014.11.058
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    References listed on IDEAS

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    1. Loiseau, Patrick & Médigue, Claire & Gonçalves, Paulo & Attia, Najmeddine & Seuret, Stéphane & Cottin, François & Chemla, Denis & Sorine, Michel & Barral, Julien, 2012. "Large deviations estimates for the multiscale analysis of heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5658-5671.
    2. Shang, Pengjian & Lu, Yongbo & Kamae, Santi, 2008. "Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 36(1), pages 82-90.
    3. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    4. Plamen Ch. Ivanov & Luís A. Nunes Amaral & Ary L. Goldberger & Shlomo Havlin & Michael G. Rosenblum & Zbigniew R. Struzik & H. Eugene Stanley, 1999. "Multifractality in human heartbeat dynamics," Nature, Nature, vol. 399(6735), pages 461-465, June.
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    Cited by:

    1. Yuan, PengCheng & Lin, XuXun, 2017. "How long will the traffic flow time series keep efficacious to forecast the future?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 419-431.
    2. Mondal, Satyajit & Gupta, Ankit, 2021. "Speed distribution for interrupted flow facility under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).

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