天气影响事故严重性？实证发现降水预测力弱，司机可能已适应恶劣天气。
The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data
- 用XGBoost模型分析50万起美国交通事故数据，优化了类别不平衡问题。
- 模型整体准确率78%，对中等严重程度事故的精度与召回率达87%。
- 意外发现：降水和能见度预测力弱，或因驾驶员已适应恶劣天气。
本研究探究环境、时间与空间因素对美国交通事故严重性的影响。基于2016至2023年间50万起交通事故数据，采用随机搜索交叉验证优化的XGBoost分类器，并通过类别权重处理类别不平衡问题。最终模型整体准确率为78%，对多数类（严重性等级2）的精确率与召回率均达87%。特征重要性分析显示，一天中的时段、地理位置以及能见度、温度、风速等气象变量是主要预测因子。然而，与初始假设相反，降水量与能见度的预测能力较弱，可能反映驾驶员在明显危险条件下已产生行为适应。数据集中以中等严重程度事故为主，限制了模型对极端案例的学习能力，提示未来需采用替代采样策略、增强特征工程并融合外部数据集。研究结果为交通管理提供实证支持，并指明严重性预测研究的新方向。
原文摘要 · Abstract (English)
This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016-2023, we trained an XGBoost classifier optimized through randomized search cross-validation and adjusted for class imbalance via class weighting. The final model achieves an overall accuracy of 78%, with strong performance on the majority class (Severity 2), attaining 87% precision and recall. Feature importance analysis reveals that time of day, geographic location, and weather-related variables, including visibility, temperature, and wind speed, rank among the strongest predictors of accident severity. However, contrary to initial hypotheses, precipitation and visibility demonstrate limited predictive power, potentially reflecting behavioral adaptation by drivers under overtly hazardous conditions. The dataset's predominance of mid-level severity accidents constrains the model's capacity to learn meaningful patterns for extreme cases, highlighting the need for alternative sampling strategies, enhanced feature engineering, and integration of external datasets. These findings contribute to evidence-based traffic management and suggest future directions for severity prediction research.
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