Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations
<|reference_start|>Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations: With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.<|reference_end|>
@article{liu2024enhancing,
    title={Enhancing Building Safety Design for Active Shooter Incidents:
  Exploration of Building Exit Parameters using Reinforcement Learning-Based
  Simulations},
    author={Ruying Liu, Wanjing Wu, Burcin Becerik-Gerber, Gale M. Lucas},
    journal={31st EG-ICE International Workshop on Intelligent Computing in
  Engineering 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10441},
    primaryClass={cs.AI cs.LG}
}