IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v578y2021ics0378437121003770.html
   My bibliography  Save this article

Iterative learning perimeter control method for traffic sub-region considering disturbances

Author

Listed:
  • Yan, Fei
  • Wang, Kun
  • Shi, Zhongke

Abstract

Most of the existing perimeter control methods in urban traffic regions are only suitable for the road network in an ideal state, and the impact of various uncertain factors and disturbances in the actual traffic system on the control performance is not considered. In this paper, a disturbance term is introduced into the vehicle balance equation of the road network, and an iterative learning perimeter control method of urban traffic area considering the disturbance is proposed by using the repeatability of the macroscopic traffic flow. Through iterative learning control of the perimeter intersections, the cumulative number of vehicles in the sub-region is stabilized near the expected value, and it is demonstrated that the tracking error of the system converges to a boundary under bounded disturbances. Finally, it is verified through simulation experiments that the proposed method can effectively suppress the effects of different levels of disturbances on the performance of the road network and improve the traffic conditions.

Suggested Citation

  • Yan, Fei & Wang, Kun & Shi, Zhongke, 2021. "Iterative learning perimeter control method for traffic sub-region considering disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    • Handle: RePEc:eee:phsmap:v:578:y:2021:i:c:s0378437121003770
    DOI: 10.1016/j.physa.2021.126104
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121003770
    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.2021.126104?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. Yan, Fei & Qiu, Jiangchen & Tian, Jianyan, 2022. "An iterative learning identification strategy for nonlinear macroscopic traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Li, Sutong & Kang, Leilei & Huang, Hao & Liu, Lan, 2023. "A perimeter control model of urban road network based on cooperative-noncooperative two-stage game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Qi, HongSheng, 2024. "Partially observable Markov decision process for perimeter control based on a stochastic macroscopic fundamental diagram," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).

    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:578:y:2021:i:c:s0378437121003770. 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: 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.