分析树木碰撞事故严重性，找出关键风险因素并揭示交互作用。
From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
- 用机器学习与回归模型识别影响事故严重性的关键因素
- 未系安全带者重伤风险接近三倍，车龄大、超速、酒驾也显著增加风险
- 发现光照、车龄、速度等多因素间存在叠加效应，适合交通安全部门参考
树木碰撞事故是道路偏离事故的重要类型，常导致致命或严重伤害，因高能量撞击所致。本研究基于2020-2023年撞树事故抽样系统（CRSS）数据库，构建综合分析框架，识别并量化撞树事故严重性的风险因素。首先采用基于机器学习的分类模型（CatBoost）识别与伤亡严重性（KA：致命或丧失行动力；BC：非丧失行动力或可能受伤）相关的因素。其次利用SHAP工具量化并可视化主要因素对事故严重性的边际影响。第三，通过二元逻辑回归模型估计因子效应，并验证SHAP的重要性度量。最后，借助SHAP交互图分析关键因素的联合影响。结果表明，未使用安全带是最具影响力的预测因子，未系安全带者因弹射风险，重伤可能性接近三倍。车辆年龄、超速违规和驾驶员受药物/酒精影响均表现出显著影响，反映车辆抗撞性下降、撞击力增强及控制能力减弱。光照条件与车辆年龄、超速与光照、安全带使用与车龄、路面状况与超速之间存在关键交互作用，呈现叠加风险效应。研究为基于安全系统的针对性干预提供了重要依据，包括加强安全带执法、在低能见度下强化限速管理、推动车辆车队更新。
原文摘要 · Abstract (English)
Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impacts. This study develops a comprehensive analytical framework to identify and quantify risk factors contributing to crash severity in tree-involved collisions using the Crash Report Sampling System (CRSS) database spanning 2020-2023. The modeling framework follows a multi-step process. First, a machine learning based classification model (CatBoost) identifies key factors associated with binary crash injury severity (KA: fatal or incapacitating injury versus BC: non-incapacitating or possible injury). Second, SHapley Additive exPlanations (SHAP) tool is used to quantify and visualize the marginal effects of top influential factors on crash severity. Third, a binary logistic regression model estimates factor effects and validates SHAP-derived importance measures. Finally, SHAP interaction plots examine the combined effects of key contributing factors. Results reveal restraint non-use as the most influential predictor, with unrestrained occupants nearly three times more likely to experience severe outcomes due to ejection risk. Vehicle age, speeding violations, and driver impairment demonstrate substantial effects, reflecting reduced crashworthiness, increased impact forces, and reduced control capabilities. Critical interactions emerge between lighting conditions and vehicle age, speeding and lighting conditions, restraint use and vehicle age, and road surface and speeding, demonstrating additive risk effects with specific interactions. These findings provide critical insights for targeted safe system-based interventions, including enhanced seat belt enforcement, speed management in reduced visibility conditions, and vehicle fleet modernization.
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