用机器学习分析火灾疏散行为，发现关键影响因素。
Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach
- 分两阶段用无监督与有监督模型识别居民疏散类型
- 车辆拥有和宠物饲养显著影响疏散方式预测准确率
- 结果可帮助制定精准应急准备与资源分配方案
火灾疏散行为受家庭资源、准备程度与情境线索的复杂交互影响。本研究基于加州、科罗拉多州和俄勒冈州居民的大规模MTurk调查，结合无监督与有监督机器学习方法，揭示潜在行为类型并预测关键疏散结果。多重对应分析、K-Modes聚类和潜类别分析识别出由车辆获取、灾害规划、技术资源、宠物饲养及居住稳定性区分的稳定子群体。互补的有监督模型显示，基于家庭特征可高精度预测交通方式；但疏散时间难以分类，因其高度依赖实时火情动态。研究深化了对火灾疏散行为的数据驱动理解，展示了机器学习在支持针对性准备策略、资源分配与公平应急规划中的潜力。
原文摘要 · Abstract (English)
Wildfire evacuation behavior is highly variable and influenced by complex interactions among household resources, preparedness, and situational cues. Using a large-scale MTurk survey of residents in California, Colorado, and Oregon, this study integrates unsupervised and supervised machine learning methods to uncover latent behavioral typologies and predict key evacuation outcomes. Multiple Correspondence Analysis, K-Modes clustering, and Latent Class Analysis reveal consistent subgroups differentiated by vehicle access, disaster planning, technological resources, pet ownership, and residential stability. Complementary supervised models show that transportation mode can be predicted with high reliability from household characteristics, whereas evacuation timing remains difficult to classify due to its dependence on dynamic, real-time fire conditions. These findings advance data-driven understanding of wildfire evacuation behavior and demonstrate how machine learning can support targeted preparedness strategies, resource allocation, and equitable emergency planning.
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