IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v190y2024ics0191261524002078.html
   My bibliography  Save this article

Kinetic Monte Carlo simulations of 1D and 2D traffic flows: Nonlocal models with generalized look-ahead rules

Author

Listed:
  • Sun, Yi

Abstract

This paper presents a study on traffic flow models in one-dimensional (1D) and two-dimensional (2D) lattices. The models incorporate generalized look-ahead rules that consider nonlocal slow-down effects. The proposed cellular automata (CA) models use stochastic rules to determine the movement of cars based on the traffic configuration ahead of each car. Specifically, a look-ahead rule is used that considers both the car density ahead and a generalized interaction function based on the distance between cars. The CA models are simulated using an efficient kinetic Monte Carlo (KMC) algorithm. The numerical results in 1D demonstrate that the flows from the KMC simulations align with the macroscopic averaged fluxes for the look-ahead rule, across various parameter settings. In the 2D results, a sharp phase transition is observed from freely flowing traffic to global jamming, depending on the initial density of cars.

Suggested Citation

  • Sun, Yi, 2024. "Kinetic Monte Carlo simulations of 1D and 2D traffic flows: Nonlocal models with generalized look-ahead rules," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:transb:v:190:y:2024:i:c:s0191261524002078
    DOI: 10.1016/j.trb.2024.103083
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261524002078
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2024.103083?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.

    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:transb:v:190:y:2024:i:c:s0191261524002078. 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.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.