IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v260y2025ics0951832025002017.html
   My bibliography  Save this article

Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms

Author

Listed:
  • Lu, Peng
  • Li, Yufei

Abstract

Fire incidents often lead to a series of social problems. Therefore, it is particularly important to optimize evacuation strategies and promote relevant social safety knowledge. Based on this, the study proposes a fire evacuation model that integrates the Fire Dynamics Simulator (FDS) with Agent-Based Modeling (ABM) to simulate a bar fire scenario. In this model, the concept of social learning is introduced, and multiple factors such as evacuation time, trampling risk, and pedestrian health are considered as risk evaluation indicators. Machine learning combined with intelligent optimization methods is applied to optimize evacuation strategies. First, we validate the effectiveness of the model by comparing the averaged simulation results with real-world data. The results demonstrate that the simulation outcomes of our model exhibit good accuracy and robustness. Secondly, we analyze the importance of the second-floor safety exit. When the second-floor safety exit remains unobstructed, evacuation efficiency and casualty risk can be significantly improved. Then, we examine the role of social knowledge. When people are aware of the fire risk and choose to evacuate immediately, casualties can be significantly reduced. Finally, we study the effectiveness of phased evacuation in enhancing crowd safety. By employing a method that combines Random Forest and the Particle Swarm Optimization-Genetic Algorithm (PSO-GA), phased evacuation strategies are optimized, resulting in definitive strategies to reduce evacuation risks. This finding further expands social knowledge, indicating that when the proportion of staggered evacuation is appropriate, evacuation risks can be significantly reduced. Our research contributes to the development of social safety knowledge and provides methodological references for formulating evacuation strategies in different settings.

Suggested Citation

  • Lu, Peng & Li, Yufei, 2025. "Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002017
    DOI: 10.1016/j.ress.2025.111000
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832025002017
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2025.111000?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:reensy:v:260:y:2025:i:c:s0951832025002017. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.