对酒后驾驶和酒后驾驶事故的风险因素分析,考虑到建筑环境
Wenhui Qin1, Shaohua Wang1, Xin Gu2
1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin, China.
International journal of injury control and safety promotion
|August 5, 2025
概括
酒后驾驶 (DUI) 和酒后驾驶 (DWI) 的风险与饮酒有关. 这项研究确定了影响这些事故的关键因素,如时间,位置和驾驶员人口统计数据,有助于制定有针对性的预防策略.
科学领域:
- 交通安全 交通安全
- 机器学习应用 机器学习应用
- 酒精障碍研究 研究 酒精障碍研究
背景情况:
- 与酒精有关的交通事故带来了重大风险.
- 有限的研究存在于影响驾驶酒后 (DUI) 和酒后驾驶 (DWI) 事件的特定因素.
- 了解这些因素对于有效的交通安全干预至关重要.
研究的目的:
- 用机器学习分析影响DUI和DWI撞车的因素.
- 为了比较增强的机器学习模型在预测碰撞风险方面的性能.
- 为了确定与酒精有关的交通事故的重大风险因素及其相互作用.
主要方法:
- 分析了中国天津市3365起与酒精有关的交通事故.
- 根据血液中酒精度对酒后驾驶和酒后驾驶事故的分类.
- 增强和比较四个机器学习模型 (CatBoost等). 使用准确度,精度,回忆和F1分数.
- 在风险因素和相互作用分析中应用沙普利添加剂解释.
主要成果:
- 增强的CatBoost模型实现了最高的性能,AUC-ROC为0.953.
- 发现的重要因素包括机时间,交叉点控制和公司密度.
- 相互作用效应显示,在高密度交叉路口地区,40-50岁的司机的酒后驾驶风险较高.
- 与汽车司机相比,两轮摩托车司机在21:00-24:00之间患醉酒驾驶的风险更高.
结论:
- 机器学习模型,特别是增强的CatBoost,有效地识别DUI和DWI风险因素.
- 特定的时间,环境和人口因素显著影响酒精相关的撞车风险.
- 这些发现支持制定有针对性的交通管理策略,以减轻DUI和DWI违规行为.
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