GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events

GPEvac：基于 GNN-PPO 的枪击事件自适应疏散路径规划

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”.

摘要： 大规模枪击事件的急剧增加凸显了对实时引导受害者安全撤离系统的迫切需求。一个有效的疏散系统必须在最大限度降低威胁暴露风险的同时，兼顾对抗性不确定性和人群拥挤动态。现有文献中的方法往往受限于特定的建筑布局策略，且在大规模场景下计算复杂度过高；而目前的实用指南通常仅建议受害者“逃跑”、“躲藏”或“反抗”。

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.

我们提出了 GPEvac：一个基于图神经网络（GNN）和近端策略优化（PPO）的框架，用于在枪击事件中计算自适应疏散路径。为了同时捕捉局部和长距离依赖关系，我们引入了一种以边为先的顺序消息传递方案，并结合了一个可学习的虚拟全局节点。由此产生的图嵌入被集成到一个置换不变的评分机制中，使得单一的学习策略能够适用于各种拓扑结构和规模的建筑布局。

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.

通过广泛的模拟实验，我们证明了 GPEvac 在不同建筑布局中均优于现有的智能基准方法，显著降低了总体的威胁暴露水平。至关重要的是，该系统在本地 CPU 硬件上仅需 14.73 毫秒即可计算出全局疏散路径，从而能够与实时监控系统无缝集成。除了在枪击事件中挽救生命外，所开发的方法论还可迁移至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。

Paper Details:

Authors: Daniel Perkins, Subhadeep Chakraborty
arXiv ID: 2609.16163
Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)

论文详情：

作者： Daniel Perkins, Subhadeep Chakraborty
arXiv ID: 2609.16163
学科分类： 人工智能 (cs.AI)；计算机与社会 (cs.CY)；机器学习 (cs.LG)；多智能体系统 (cs.MA)；系统与控制 (eess.SY)