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PrioritEvac: an Agent-Based Model (ABM) for Examining Social Factors of Building Fire Evacuation

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  • Eileen Young

    (University of Delaware)

  • Benigno Aguirre

    (University of Delaware)

Abstract

Fire evacuation modeling benefits from the application of social science both in terms of accuracy and external validation. This paper describes PrioritEvac, a novel agent-based model which incorporates the social dimension of group loyalty into fire evacuation and responses to fire and smoke. It uses individual priorities, making for a dynamic approach that allows greater agency and nuance. PrioritEvac is programmed in NetLogo and validated using extensive data collected from the Station nightclub fire. The statistical analysis of the results of the model indicate that, compared to historical patterns, it reproduces along multiple metrics including a mean of 114 deaths (std. dev. = 38) over 50 runs, which puts the actual result of the fire within one standard deviation of the mean results of the simulation. Overall, the mean differential along all the metrics is 79, significantly outperforming all published ABM models of the Station nightclub fire that did not incorporate social relationships.

Suggested Citation

  • Eileen Young & Benigno Aguirre, 2021. "PrioritEvac: an Agent-Based Model (ABM) for Examining Social Factors of Building Fire Evacuation," Information Systems Frontiers, Springer, vol. 23(5), pages 1083-1096, September.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:5:d:10.1007_s10796-020-10074-9
    DOI: 10.1007/s10796-020-10074-9
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    References listed on IDEAS

    as
    1. Sherif El-Tawil & Jieshi Fang & Benigno Aguirre & Eric Best, 2017. "A Computational Study of the Station Nightclub Fire Accounting for Social Relationships," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-10.
    2. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    3. B. E. Aguirre & Manuel R. Torres & Kimberly B. Gill & H. Lawrence Hotchkiss, 2011. "Normative Collective Behavior in The Station Building Fire," Social Science Quarterly, Southwestern Social Science Association, vol. 92(1), pages 100-118, March.
    4. Isobe, Motoshige & Adachi, Taku & Nagatani, Takashi, 2004. "Experiment and simulation of pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 638-650.
    5. Jule Thober & Birgit Müller & Jürgen Groeneveld & Volker Grimm, 2017. "Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-8.
    6. Gaganmeet Kaur Awal & K. K. Bharadwaj, 2019. "Leveraging collective intelligence for behavioral prediction in signed social networks through evolutionary approach," Information Systems Frontiers, Springer, vol. 21(2), pages 417-439, April.
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    Cited by:

    1. Yuko Murayama & Hans Jochen Scholl & Dimiter Velev, 2021. "Information Technology in Disaster Risk Reduction," Information Systems Frontiers, Springer, vol. 23(5), pages 1077-1081, September.

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