IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v583y2021ics037843712100529x.html
   My bibliography  Save this article

Obstacle avoidance in the improved social force model based on ant colony optimization during pedestrian evacuation

Author

Listed:
  • Yang, Xiaoli
  • Yang, Xiaoxia
  • Li, Yongxing
  • Zhang, Jihui
  • Kang, Yuanlei

Abstract

Obstacle avoidance behavior, as an important part of pedestrian evacuation, is of great significance to the study of evacuation efficiency. This paper investigates the definitions of desired direction in the social force model for obstacle avoidance of two types of pedestrians, including pedestrians with and without complete evacuation information. Ant colony optimization algorithm is adopted to navigate pedestrians with complete information. Herding behavior, individual preference affected by obstacles and walls are taken into consideration when defining the desired direction of pedestrians with local information. Simulation experiments are carried out to explore obstacle avoidance dynamics, the effects of herding behavior and visibility. Results indicate that pedestrians with complete information can not only avoid obstacles better, but also have the ability to choose the shorter route to the exit which could improve the leaving efficiency. Meanwhile, the trajectories of pedestrians with local information are always accompanied by some twists and turns, which could obviously lead to a waste of time. No matter whether the proportion of pedestrians with complete information is large or not, herding behavior can make the trajectories of pedestrians with local information smoother, and the individual behavior can make their trajectories more curved. Moreover, the larger the visibility radius is, the smoother trajectories become and the greater effective displacements for pedestrians with local information are.

Suggested Citation

  • Yang, Xiaoli & Yang, Xiaoxia & Li, Yongxing & Zhang, Jihui & Kang, Yuanlei, 2021. "Obstacle avoidance in the improved social force model based on ant colony optimization during pedestrian evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    • Handle: RePEc:eee:phsmap:v:583:y:2021:i:c:s037843712100529x
    DOI: 10.1016/j.physa.2021.126256
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712100529X
    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.2021.126256?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. Jiang, Yan-Qun & Hu, Ying-Gang & Huang, Xiaoqian, 2022. "Modeling pedestrian flow through a bottleneck based on a second-order continuum model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Liu, Yulu & Ma, Xuechen & Tao, Yizhou & Dong, Liyun & Ding, Xu & Qiu, Xiang, 2024. "Numerical investigation on the impact of obstacles on phase transition in pedestrian counter-flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    3. Siyuan Ma & Yongqing Guo & Fulu Wei & Qingyin Li & Zhenyu Wang, 2022. "An Improved Social Force Model of Pedestrian Twice–Crossing Based on Spatial–Temporal Trajectory Characteristics," Sustainability, MDPI, vol. 14(24), pages 1-14, December.

    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:583:y:2021:i:c:s037843712100529x. 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.