AI-Assisted VR Simulation for Predicting and Minimizing Evacuation Congestion in School Shooting Emergencies
Keywords:
VR Evacuation Simulation, AI Congestion Prediction, Adaptive PathfindingAbstract
School emergencies involving violent attacks often lead to unpredictable evacuation behavior. Panic, congestion, and avoidance of the threat can direct students away from the nearest exits and create dangerous bottlenecks. Existing evacuation systems and building designs are typically evaluated using static routing methods that do not adapt to real-time crowd conditions. This limits their ability to prevent or reduce congestion during an active emergency. The study proposes the development of an AI-assisted, Unity-based virtual reality (VR) evacuation simulator that models student evacuation behavior during a simulated school-shooting emergency at the Systems Plus College Foundation (SPCF) campus. The system integrates a Gated Recurrent Unit (GRU) neural network to predict congestion in critical corridors and exits. It also utilizes the D* Lite adaptive pathfinding algorithm that reroutes evacuees away from forecasted bottlenecks in real time. The proposed GRU–D* Lite approach will be benchmarked against conventional A* routing across four performance measures: evacuation time, congestion level, route utilization, and evacuation success rate. The study will also test alternative building configurations, such as additional exits and external emergency stairs, to evaluate their effect on congestion. Following a Descriptive-Developmental research design implemented through the V-Model, the system is expected to demonstrate improved evacuation efficiency over static routing and provide the Physical Plant and Facilities Office (PPFO) with a safe, data-driven tool for evaluating evacuation plans and congestion-mitigation strategies.
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