IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v370y2006i2p769-776.html
   My bibliography  Save this article

The application of Hölder exponent to traffic congestion warning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437106002500
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2006.02.032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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.
    6. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mulligan, Robert F., 2004. "Fractal analysis of highly volatile markets: an application to technology equities," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(1), pages 155-179, February.
    2. Kwapień, J. & Drożdż, S. & Oświe¸cimka, P., 2006. "The bulk of the stock market correlation matrix is not pure noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 589-606.
    3. Chen, Feier & Tian, Kang & Ding, Xiaoxu & Miao, Yuqi & Lu, Chunxia, 2016. "Finite-size effect and the components of multifractality in transport economics volatility based on multifractal detrending moving average method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1058-1066.
    4. Brandi, Giuseppe & Di Matteo, T., 2022. "Multiscaling and rough volatility: An empirical investigation," International Review of Financial Analysis, Elsevier, vol. 84(C).
    5. Kwapień, J. & Oświe¸cimka, P. & Drożdż, S., 2005. "Components of multifractality in high-frequency stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 466-474.
    6. Suárez-García, Pablo & Gómez-Ullate, David, 2014. "Multifractality and long memory of a financial index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 226-234.
    7. Eisler, Z. & Kertész, J., 2004. "Multifractal model of asset returns with leverage effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 603-622.
    8. Călin Vamoş & Maria Crăciun & Nicolae Suciu, 2015. "Automatic algorithm to decompose discrete paths of fractional Brownian motion into self-similar intrinsic components," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(10), pages 1-10, October.
    9. Xiong, Gang & Zhang, Shuning & Yang, Xiaoniu, 2012. "The fractal energy measurement and the singularity energy spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6347-6361.
    10. Yang, Yan-Hong & Xie, Wen-Jie & Li, Ming-Xia & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2017. "Statistical properties of user activity fluctuations in virtual worlds," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 271-278.
    11. Xiong, Gang & Zhang, Shuning & Liu, Qiang, 2012. "The time-singularity multifractal spectrum distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4727-4739.
    12. Serrano, E. & Figliola, A., 2009. "Wavelet Leaders: A new method to estimate the multifractal singularity spectra," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2793-2805.
    13. Grahovac, Danijel & Leonenko, Nikolai N., 2014. "Detecting multifractal stochastic processes under heavy-tailed effects," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 78-89.
    14. Mandelbrot, Benoit B., 1999. "Renormalization and fixed points in finance, since 1962," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 263(1), pages 477-487.
    15. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    16. Xiong, Gang & Yu, Wenxian & Xia, Wenxiang & Zhang, Shuning, 2016. "Multifractal signal reconstruction based on singularity power spectrum," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 25-32.
    17. Eric M. Aldrich & Indra Heckenbach & Gregory Laughlin, 2014. "A Compound Multifractal Model for High-Frequency Asset Returns," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-05, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    18. Benoit B. Mandelbrot, 2005. "Parallel cartoons of fractal models of finance," Annals of Finance, Springer, vol. 1(2), pages 179-192, October.
    19. Dashtian, Hassan & Jafari, G. Reza & Sahimi, Muhammad & Masihi, Mohsen, 2011. "Scaling, multifractality, and long-range correlations in well log data of large-scale porous media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2096-2111.
    20. Pawe{l} O'swik{e}cimka & Stanis{l}aw Dro.zd.z & Mattia Frasca & Robert Gk{e}barowski & Natsue Yoshimura & Luciano Zunino & Ludovico Minati, 2020. "Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses," Papers 2004.03319, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:370:y:2006:i:2:p:769-776. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.