用可解释AI分析美国行人死亡主因，找出高发因素
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI
- 用XAI技术对比高低事故率州的行人死亡数据
- 年龄、酒药使用、环境差是关键风险因素，模型准确率达98%
- 结果对城市规划和交通执法有直接指导价值
道路死亡事故是全球性的公共安全挑战，行人因身体与性能差异在车行碰撞中尤为脆弱。本研究采用可解释人工智能（XAI）分析2018-2022年间美国五个事故率最高与最低州的行人死亡模式。基于车祸伤亡报告系统（FARS）数据，运用决策树、梯度提升树、随机森林及XGBoost等机器学习方法预测致死因素。为缓解数据不平衡问题，采用合成少数类过采样技术（SMOTE），并通过SHapley加性解释（SHAP）提升模型可解释性。结果显示，年龄、饮酒吸毒、地点与环境条件是显著预测因子。其中XGBoost模型表现最佳，平衡准确率达98%，准确率90%、精确率92%、召回率90%、F1得分为91%。研究发现，行人事故多发于路段中间及能见度低区域，老年人与受药物影响者风险更高。这些发现可为政策制定者和城市规划者提供依据，推动改善照明、强化人行设施、加强交通执法等针对性安全措施，以降低死亡率，提升公共安全。
原文摘要 · Abstract (English)
Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018-2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques-including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost-to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98 %, accuracy of 90 %, precision of 92 %, recall of 90 %, and an F1 score of 91 %. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety.
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