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Agent-Based Emergency Evacuation Simulation with Individuals with Disabilities in the Population

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Author Info
Keith Christensen ()
Yuya Sasaki ()
Abstract

Catastrophic events have raised numerous issues concerning how effectively the built environment accommodates the evacuation needs of individuals with disabilities. Individuals with disabilities represent a significant, yet often overlooked, portion of the population disproportionately affected in emergency situations. Incorporating disability considerations into emergency evacuation planning, preparation, and other activities is critical. The most widely applied method used to evaluate how effectively the built environment accommodates emergency evacuations is agent-based or microsimulation modeling. However, current evacuation models do not adequately address individuals with disabilities in their simulated populations. This manuscript describes the BUMMPEE model, an agent-based simulation capable of classifying the built environment according to environmental characteristics and simulating a heterogeneous population according to variation in individual criteria. The method allows for simulated behaviors which more aptly represent the diversity and prevalence of disabilities in the population and their interaction with the built environment. Comparison of the results of an evacuation simulated using the BUMMPEE model is comparable to a physical evacuation with a similar population and setting. The results of the comparison indicate that the BUMMPEE model is a reasonable approach for simulating evacuations representing the diversity and prevalence of disability in the population

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File URL: http://jasss.soc.surrey.ac.uk/11/3/9.html
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Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 11 (2008)
Issue (Month): ()
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Handle: RePEc:jas:jasssj:2007-8-3

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Related research
Keywords: Agent-Based Simulation Individual-Based Simulation Disability Emergency Egress Evacuation Reinforcement Learning

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This page was last updated on 2008-9-25.


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