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Multifractal and singularity analysis of highway volume data

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  • Dai, Meifeng
  • Zhang, Cheng
  • Zhang, Danping

Abstract

Recent work has shown that the mathematics of multifractal can be used to provide a quantitative signature in many fields. In this paper, we investigate the traffic time series for volume data observed on Guangshen highway. Firstly, we find there exists a multifractal behavior in the traffic data, and the data on both work days and rest days have similar multifractality. Then, we study the singularity of these data. A singularity exponent method based on multifractal theory is proposed to extract or classify singular data, which is more precise and clear than the Hölder exponent method.

Suggested Citation

  • Dai, Meifeng & Zhang, Cheng & Zhang, Danping, 2014. "Multifractal and singularity analysis of highway volume data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 332-340.
    • Handle: RePEc:eee:phsmap:v:407:y:2014:i:c:p:332-340
    DOI: 10.1016/j.physa.2014.04.005
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2020. "Multiscale multifractal analysis on air traffic flow time series: A single airport departure flight case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. He, Hong-di & Wang, Jun-li & Wei, Hai-rui & Ye, Cheng & Ding, Yi, 2016. "Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 518-525.

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