天气悖论：为何降水无法预测大规模美国数据中的交通事故严重性
The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data
AI总结：
本研究发现降水和能见度对交通事故严重性预测效果有限，可能由于驾驶员行为适应，需改进采样策略和特征工程以提升预测能力。
摘要：
本研究探讨了环境、时间与空间因素对美国交通事故严重性的预测能力。使用涵盖2016至2023年的50万起美国交通事故数据集，我们训练了一个通过随机搜索交叉验证优化的XGBoost分类器，并通过类别加权调整类别不平衡问题。最终模型达到78%的整体准确率，对多数类（严重性2）表现强劲，达到87%的精确率和召回率。特征重要性分析显示，时间、地理位置以及与天气相关的变量，包括能见度、温度和风速，是事故严重性的最强预测因素。然而，与初始假设相反，降水和能见度的预测能力有限，可能反映了驾驶员在明显危险条件下的行为适应。数据集中多数为中等严重性事故，限制了模型学习极端案例有意义模式的能力，凸显了需要替代采样策略、增强特征工程和整合外部数据集的必要性。这些发现为基于证据的交通管理提供了依据，并为严重性预测研究指明了未来方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
