Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations

TL;DR

This paper introduces a reinforcement learning simulation to analyze how building exit configurations affect safety during active shooter incidents, emphasizing the importance of exit placement and availability.

Contribution

It presents a novel reinforcement learning-based simulation that models dynamic shooter behaviors to evaluate building exit strategies for improved safety.

Findings

More exits improve evacuation and reduce harm

Proximity of exits to shooter impacts safety outcomes

Dynamic shooter behavior modeling provides new safety insights

Abstract

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.