发表机构
The Bredesen Center for Interdisciplinary Research and Graduate Education; Department of Mechanical and Aerospace Engineering, University of Tennessee（布雷德森跨学科研究与研究生教育中心; 田纳西大学机械与航空航天工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对枪击事件中疏散路径规划问题，提出基于GNN的PPO框架GPEvac，通过边优先消息传递和排列不变评分实现跨布局自适应疏散，显著降低威胁暴露，计算仅需14.73毫秒。
AI 中文摘要
大规模枪击事件的急剧增加凸显了实时引导受害者到达安全地点的系统的迫切需求。有效的疏散系统必须最小化威胁暴露，同时还要考虑对抗性不确定性和拥挤动态。现有文献中的方法严格受限于特定布局的策略，在大规模布局中计算上难以处理，而实际指导方针仅建议受害者“跑”、“藏”或“战斗”。我们提出GPEvac：一种基于GNN的PPO框架，用于在枪击事件期间计算自适应疏散路线。为了捕捉局部和长距离依赖，我们引入了一种具有可学习虚拟全局节点的边优先顺序消息传递方案。由此产生的图嵌入被集成到排列不变的评分机制中，使得单一学习策略能够在不同拓扑和规模的建筑布局中运行。通过大量模拟，我们表明GPEvac在多种不同建筑布局中优于智能基线，显著降低了总威胁暴露。关键的是，该系统在本地CPU硬件上仅需14.73毫秒即可计算全局疏散路线，使其能够与实时监控系统无缝集成。除了在枪击事件中拯救生命外，所开发的方法还可迁移到其他图结构决策领域，包括关键基础设施、智能交通系统和自适应传感器网络。
英文摘要
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.
Comments7 pages, 4 figures, 3 tables
