针对突发枪击事件设计了考虑网络容量的多路径疏散算法，显著降低伤亡与拥堵。
An Optimized Evacuation Plan for an Active-Shooter Situation Constrained by Network Capacity
- 基于路径容量约束优化多条安全疏散路线
- 相比无容量限制算法减伤34.16%，比专家建议策略减伤53.3%
- 关键瓶颈节点占用率下降约50%，适合应急规划与建筑安全设计
2016至2022年间，美国发生超过3400起公共枪击事件，其中25.1%发生在教育机构，29.4%在工作场所（包括办公楼），19.6%在零售商店，13.4%在餐厅和酒吧。在这些紧急情况下，正确决策可能决定生死。然而，疏散过程高度紧张，缺乏可验证的实时信息可能导致致命误判。为此，我们开发了一种多路径路由优化算法，为每位疏散者确定多条最优安全路径，同时考虑路径可用容量，减少拥挤与瓶颈风险。结果表明，该算法相比无容量约束的先前算法，总伤亡减少34.16%；相比专家建议策略，伤亡减少53.3%。此外，通过减少拥堵，关键瓶颈节点的占用率相比其他两种算法均降低约50%。
原文摘要 · Abstract (English)
A total of more than 3400 public shootings have occurred in the United States between 2016 and 2022. Among these, 25.1% of them took place in an educational institution, 29.4% at the workplace including office buildings, 19.6% in retail store locations, and 13.4% in restaurants and bars. During these critical scenarios, making the right decisions while evacuating can make the difference between life and death. However, emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions. To tackle this problem, we developed a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee while accounting for available capacity along the route, thus reducing the threat of crowding and bottlenecking. Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively. Further, our approach to reduce crowding resulted in an approximate 50% reduction in occupancy in key bottlenecking nodes compared to both of the other evacuation algorithms.
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