2024 – Present
This project aims to expand upon the limited library of detection and response strategies available regarding active shooter attacks. The objective is to create an algorithm and user-configurable software application capable of extending insights gained from collected data to optimize crisis response strategies. Developing new strategies will help researchers effectively create training modules, evaluate buildings, and provide crisis response guidance to civilians and law enforcement.
This project is gathering new data and leveraging existing datasets on human response to simulated active shooter incidents (such as those occurring at academic and church buildings) to better understand how people will behave in realistic active shooter situations, considering factors like building layouts, number of shooters, and firearm characteristics. The project intends to apply its findings by developing a software application to include interactive configurations and environments to simulate scenarios and generate visual outputs to optimize safety protocols specific to the venue. This tool will allow stakeholders, including DHS and law enforcement agencies, to assess and optimize safety protocols in public spaces like schools and churches to react to active shooter incidents.
Limited existing studies attempt to model individual civilian response behaviors, and many of those studies have made straightforward assumptions that may not accurately reflect reality. This project intends to develop a novel generative model that can provide intuitive insights for a learning environment and simulate behaviors under new circumstances and environments. This approach aims to provide a more nuanced understanding of how different factors influence civilian behaviors in crisis situations.
In 2024, project researchers have developed a novel technique to identify potential risks during an active shooter attack using synthetic human behavioral data. Testing and validation of the performance of developed techniques under multiple simulated environments is underway.
Researchers work closely with Mississippi law enforcement, DHS, and other SENTRY projects, including the Protecting Soft Targets: a Game-Theoretic Framework for Multi-Target, Multi-Layer Defense Against Strategic Attackers project and the Real-Time Crowd and Attacker Forecasting for Risk Assessment and Threat Mitigation project. These partnerships allow researchers to collect feedback, validate data, and improve the proposed tool.
