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The application of Hölder exponent to traffic congestion warning

Author

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  • Shang, Pengjian
  • Lu, Yongbo
  • Kama, Santi

Abstract

In this paper, we applied multifractal modeling techniques to analyze the traffic data collected from the Beijing Yuquanying. The results indicated that multifractal characteristics obviously exist in the traffic system; the degree of fractality of these traffic data tends to increase as the traffic system becomes congested; the Hölder exponent that measures the local rate of fractality may be used as indicators to predict the presence of the traffic congestion.

Suggested Citation

  • Shang, Pengjian & Lu, Yongbo & Kama, Santi, 2006. "The application of Hölder exponent to traffic congestion warning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 769-776.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:2:p:769-776
    DOI: 10.1016/j.physa.2006.02.032
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    References listed on IDEAS

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    1. Laurent Calvet & Adlai Fisher & Benoit Mandelbrot, 1997. "Large Deviations and the Distribution of Price Changes," Cowles Foundation Discussion Papers 1165, Cowles Foundation for Research in Economics, Yale University.
    2. Castro e Silva, A. & Moreira, J.G., 1997. "Roughness exponents to calculate multi-affine fractal exponents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 235(3), pages 327-333.
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    Citations

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    Cited by:

    1. Li, Ming & Zhang, Peidong & Leng, Jianxing, 2016. "Improving autocorrelation regression for the Hurst parameter estimation of long-range dependent time series based on golden section search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 189-199.
    2. Chattopadhyay, Anirban & Khondekar, Mofazzal H. & Bhattacharjee, Anup Kumar, 2018. "Fractality and singularity in CME linear speed signal: Cycle 23," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 542-550.
    3. Li, Ming & Zhao, Wei, 2012. "Quantitatively investigating the locally weak stationarity of modified multifractional Gaussian noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6268-6278.
    4. 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.
    5. Dai, Meifeng & Hou, Jie & Ye, Dandan, 2016. "Multifractal detrended fluctuation analysis based on fractal fitting: The long-range correlation detection method for highway volume data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 722-731.
    6. Li, Ming & Zhao, Wei, 2013. "On bandlimitedness and lag-limitedness of fractional Gaussian noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 1955-1961.
    7. Xu, Kaiye & Shang, Pengjian & Feng, Guochen, 2015. "Multifractal time series analysis using the improved 0–1 test model," Chaos, Solitons & Fractals, Elsevier, vol. 70(C), pages 134-143.

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