IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v93y2020i3d10.1140_epjb_e2020-100469-5.html
   My bibliography  Save this article

The impact of the predictive effect on traffic dynamics in a lattice model with passing

Author

Listed:
  • Daljeet Kaur

    (School of Mathematics, Thapar Institute of Engineering and Technology)

  • Sapna Sharma

    (School of Mathematics, Thapar Institute of Engineering and Technology)

Abstract

In this paper, we study the impact of the predictive effect with passing for a unidirectional single-lane highway. The model is further analyzed theoretically by the means of stability analysis. The influence of the predictive effect is examined on traffic stream stability through linear stability analysis when passing is permitted. It is shown that the accurate expected behavior of the vehicles ahead can enhance the stability of traffic flow for any rate of passing. Using nonlinear stability analysis, we obtained the critical value of passing constant for which kink soliton solution of mKdV equation exist. When the passing constant is smaller than critical value, the jamming transition occurs between uniform flow and kink flow while for higher value of passing constant, the jamming transitions occur from uniform flow to kink density wave flow through a chaotic phase. Numerical simulation verifies the theoretical predictions which confirms that the traffic congestion can be suppressed efficiently by considering the predictive effect in a single-lane traffic system when passing is allowed. Graphical abstract

Suggested Citation

  • Daljeet Kaur & Sapna Sharma, 2020. "The impact of the predictive effect on traffic dynamics in a lattice model with passing," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(3), pages 1-10, March.
    • Handle: RePEc:spr:eurphb:v:93:y:2020:i:3:d:10.1140_epjb_e2020-100469-5
    DOI: 10.1140/epjb/e2020-100469-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/e2020-100469-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1140/epjb/e2020-100469-5?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.

    Citations

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


    Cited by:

    1. Madaan, Nikita & Sharma, Sapna, 2022. "Delayed-feedback control in multi-lane traffic system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    2. Kaur, Daljeet & Sharma, Sapna & Gupta, Arvind Kumar, 2022. "Analyses of lattice hydrodynamic area occupancy model for heterogeneous disorder traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Huimin Liu & Rongjun Cheng & Tingliu Xu, 2021. "Analysis of a Novel Two-Dimensional Lattice Hydrodynamic Model Considering Predictive Effect," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    4. Verma, Muskan & Sharma, Sapna, 2022. "Chaotic jam and phase transitions in a lattice model with density dependent passing," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Nikita Madaan & Sapna Sharma, 2022. "Influence of driver’s behavior with empirical lane changing on the traffic dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(1), pages 1-11, January.
    6. Zhai, Cong & Wu, Weitiao, 2021. "A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

    More about this item

    Keywords

    Statistical and Nonlinear Physics;

    Statistics

    Access and download statistics

    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:spr:eurphb:v:93:y:2020:i:3:d:10.1140_epjb_e2020-100469-5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.