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预测埃塞俄比亚西北部的车祸严重程度:利用驾驶员,环境和道路状况的机器学习方法

Abraham Keffale Mengistu¹, Andualem Enyew Gedefaw², Nebebe Demis Baykemagn²

¹Department of Health Informatics, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia. abreham_keffale@dmu.edu.et.

Scientific reports

|July 2, 2025

概括

机器学习准确地预测了埃塞俄比亚西北部的道路交通事故严重程度. 关键因素包括驾驶员年龄和环境条件,指导有针对性的安全干预.

科学领域:

公共卫生公共卫生
数据科学数据科学数据科学
交通运输安全运输安全

背景情况:

埃塞俄比亚西北部的道路交通事故 (RTA) 是一个重大的公共卫生问题,死亡率很高.
基础设施赤字和环境危害加剧了该地区的RTA严重程度.
低收入和中等收入国家 (LMICs) 缺乏针对事故严重性的本地预测模型.

研究的目的:

用机器学习技术预测埃塞俄比亚西北部道路交通事故的严重程度.
确定导致该地区RTA严重性的关键因素.
评估各种机器学习模型对RTA严重性预测的有效性.

主要方法:

利用来自埃塞俄比亚西北部警察报告的2000个RTA (2018-2023) 的数据集.
将驾驶员的人口统计,行为因素和环境条件整合到分析中.
采用并比较了十个机器学习模型,包括随机森林,XGBoost和LightGBM,在使用合成少数群体过量采样技术 (SMOTE) 解决类不平衡后.
使用超参数调整优化模型和使用Shapley添加式解释 (SHAP) 评估可解释性.

主要成果:

随机森林模型在调整后获得了最高的准确度 (82%) 和AUC-ROC (0.87).
司机年龄 (平均44岁) 和环境因素 (夜间,未照明的道路,雨) 是致命事故的重要预测因素.
在SMOTE中,随机森林的准确度从78.6%提高到82%.
集合方法,特别是随机森林,在关键指标上表现出卓越的表现.

结论:

机器学习对于在LMICs中对RTA严重程度进行上下文化是有效的.
随机森林模型为埃塞俄比亚西北部的政策制定者提供了一个强大的工具.
建议包括改善道路基础设施,实施清醒检查站,加强摩托车安全,以符合可持续发展目标3.6.

关键词:

汽车事故严重程度的严重程度埃塞俄比亚埃塞俄比亚埃塞俄比亚机器学习是机器学习.道路交通安全问题道路安全问题这就是 SHAP SHAP 的意思.在SMOTE中使用.

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