GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events
cs.AI, cs.CY, cs.LG, cs.MA, cs.SY, eess.SY
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: 7 pages, 4 figures, 3 tables
License: http://creativecommons.org/licenses/by/4.0/
The gist: The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time.
Terminology
Abstract
The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time. An effective evacuation system must minimize threat exposure while also accounting for adversarial uncertainty and crowding dynamics. Current methods in the literature are rigidly constrained to layout-specific policies and computationally intractable in large-scale layouts, while practical guidelines simply advise victims to "run", "hide", or "fight". We propose GPEvac: a GNN-based PPO framework that computes adaptive evacuation routes during shooting events. To capture both local and long-distance dependencies, we introduce an edge-first sequential message-passing scheme with a learnable virtual global node. The resulting graph embeddings are integrated into a permutation-invariant scoring mechanism that allows a single learned policy to operate across building layouts of diverse topologies and sizes. Through extensive simulation, we show that GPEvac outperforms intelligent baselines across distinct architectural layouts, significantly reducing total threat exposure. Crucially, the system computes global evacuation routes in just 14.73 ms on local CPU hardware, enabling seamless integration with live surveillance systems. In addition to saving lives during shooting events, the methodologies developed are transferable to other graph-structured decision-making domains, including critical infrastructure, intelligent transportation systems, and adaptive sensor networks.
Sources
- Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
- Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification
- Playing Atari with Deep Reinforcement Learning
- Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
- You Only Look Once: Unified, Real-Time Object Detection
- Proximal Policy Optimization Algorithms
- Active shooter detection and robust tracking utilizing supplemental synthetic data
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