用机器学习分析个人因素如何影响应对枪击事件的反应，发现训练方式和方向感最关键。
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning
- 通过可解释机器学习分析107人数据，研究训练方式、方向感等对反应的影响。
- 虚拟现实训练优于视频训练，方向感好者更倾向逃跑且暴露时间更短。
- 女性逃生准备时间更长，可能因风险感知更高，适合安全设计与培训优化参考。
本研究通过可解释机器学习方法，利用107名参与者的数据填补了个人因素如何影响建筑内人员在枪击事件中反应的知识空白。研究考察了训练方式、先前训练经历、方向感和性别等因素。反应表现包括决策（逃跑、躲避、混合）、脆弱性（暴露于枪手的时间）及预疏散时间。结果表明，逃跑倾向显著决定整体应对策略，超过脆弱性和预疏散时间的影响；基于虚拟现实的训练效果优于视频训练；方向感较好和有经验者更倾向于逃跑且暴露时间更短；性别虽对决策和脆弱性影响较小，但显著影响预疏散时间，女性疏散准备时间更长，可能源于更高的风险感知。研究强调了个人因素在应对枪击事件中的关键作用。
原文摘要 · Abstract (English)
This study bridges the knowledge gap on how personal factors affect building occupants' responses in active shooter situations by applying interpretable machine learning methods to data from 107 participants. The personal factors studied are training methods, prior training experience, sense of direction, and gender. The response performance measurements consist of decisions (run, hide, multiple), vulnerability (corresponding to the time a participant is visible to a shooter), and pre-evacuation time. The results indicate that the propensity to run significantly determines overall response strategies, overshadowing vulnerability, and pre-evacuation time. The training method is a critical factor where VR-based training leads to better responses than video-based training. A better sense of direction and previous training experience are correlated with a greater propensity to run and less vulnerability. Gender slightly influences decisions and vulnerability but significantly impacts pre-evacuation time, with females evacuating slower, potentially due to higher risk perception. This study underscores the importance of personal factors in shaping responses to active shooter incidents.
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