IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v599y2022ics0378437122002850.html
   My bibliography  Save this article

Spatial correlation analysis of traffic flow on parallel motorways in Germany

Author

Listed:
  • Gartzke, Sebastian
  • Wang, Shanshan
  • Guhr, Thomas
  • Schreckenberg, Michael

Abstract

With the widely used method of correlation matrix analysis, this study reveals the change of traffic states on parallel motorways in North Rhine-Westphalia, Germany. In terms of the time series of traffic flow and velocity, we carry out a quantitative analysis in correlations and reveal a high level of strongly positive traffic flow correlation and rich structural features in the corresponding correlation matrices. The strong correlation is mainly ascribed to the daily time evolution of traffic flow during the periods of rush hours and non-rush hours. In terms of free flow and congestion, the structural features are able to capture the average traffic situation we derive from our data. Furthermore, the structural features in correlation matrices for individual time periods corroborate our results from the correlation matrices regarding a whole day. The average correlations in traffic flows and velocities over all pairwise sections disclose the traffic behavior during each individual time period. Our contribution uncovers the potential application of correlation analysis on the study of traffic networks as a complex system.

Suggested Citation

  • Gartzke, Sebastian & Wang, Shanshan & Guhr, Thomas & Schreckenberg, Michael, 2022. "Spatial correlation analysis of traffic flow on parallel motorways in Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    • Handle: RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122002850
    DOI: 10.1016/j.physa.2022.127367
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122002850
    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.2022.127367?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. Yuriy Stepanov & Philip Rinn & Thomas Guhr & Joachim Peinke & Rudi Schafer, 2015. "Stability and Hierarchy of Quasi-Stationary States: Financial Markets as an Example," Papers 1503.00556, arXiv.org.
    2. Michael C. Munnix & Takashi Shimada & Rudi Schafer & Francois Leyvraz Thomas H. Seligman & Thomas Guhr & H. E. Stanley, 2012. "Identifying States of a Financial Market," Papers 1202.1623, arXiv.org.
    3. Anton J. Heckens & Sebastian M. Krause & Thomas Guhr, 2020. "Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion," Papers 2004.12336, arXiv.org, revised Sep 2020.
    4. Philip Rinn & Yuriy Stepanov & Joachim Peinke & Thomas Guhr & Rudi Schafer, 2015. "Dynamics of quasi-stationary systems: Finance as an example," Papers 1502.07522, arXiv.org.
    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. Wang, Shanshan & Schreckenberg, Michael & Guhr, Thomas, 2023. "Response functions as a new concept to study local dynamics in traffic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

    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. Martin He{ss}ler & Tobias Wand & Oliver Kamps, 2023. "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation," Papers 2308.00087, arXiv.org.
    2. Heckens, Anton J. & Guhr, Thomas, 2022. "New collectivity measures for financial covariances and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Pharasi, Hirdesh K. & Seligman, Eduard & Sadhukhan, Suchetana & Majari, Parisa & Seligman, Thomas H., 2024. "Dynamics of market states and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    4. Tobias Wand & Oliver Kamps & Hiroshi Iyetomi, 2024. "Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition," Papers 2408.12839, arXiv.org.
    5. M. Mija'il Mart'inez-Ramos & Parisa Majari & Andres R. Cruz-Hern'andez & Hirdesh K. Pharasi & Manan Vyas, 2024. "Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm," Papers 2402.05364, arXiv.org, revised Jun 2024.
    6. Hirdesh K. Pharasi & Suchetana Sadhukhan & Parisa Majari & Anirban Chakraborti & Thomas H. Seligman, 2021. "Dynamics of the market states in the space of correlation matrices with applications to financial markets," Papers 2107.05663, arXiv.org.
    7. Nick James & Kevin Chin, 2021. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Papers 2111.11022, arXiv.org, revised Jan 2022.
    8. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    9. K. Kanjamapornkul & R. Pinv{c}'ak, 2016. "Kolmogorov Space in Time Series Data," Papers 1606.03901, arXiv.org.
    10. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.
    12. James, Nick & Menzies, Max & Chin, Kevin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    13. Nick James & Max Menzies & Kevin Chin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Papers 2203.15911, arXiv.org, revised Sep 2022.
    14. James, Nick & Chin, Kevin, 2022. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    15. James, Nick & Menzies, Max, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    16. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
    17. Nick James & Max Menzies & Georg A. Gottwald, 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Papers 2202.10623, arXiv.org, revised Jun 2022.
    18. Nick James & Max Menzies, 2021. "Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time," Papers 2107.13926, arXiv.org, revised Dec 2021.
    19. Nick James, 2021. "Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities," Papers 2112.15321, arXiv.org, revised Mar 2022.
    20. Artur Sokolovsky & Luca Arnaboldi, 2020. "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components," Papers 2009.09993, arXiv.org, revised Jun 2022.

    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:599:y:2022:i:c:s0378437122002850. 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.