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基于可解释的机器学习框架,预测和分析两轮摩托车和车辆碰撞事故中骑手受伤严重程度的因素分析

Tianzheng Wei¹, Tong Zhu², Miao Lin³

¹School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan, China.

Traffic injury prevention

|November 29, 2023

概括

提高摩托车安全至关重要. 这项研究确定了影响摩托车驾驶员受伤严重程度的关键因素,如驾驶员里程和投距离,有助于预防事故.

科学领域:

道路交通安全道路交通安全
交通事故分析交通事故分析
公共卫生公共卫生

背景情况:

两轮摩托车驾驶员是脆弱的道路使用者,这使得减少事故伤害成为重大的公共卫生问题.
准确识别影响碰撞严重性的因素对于减轻伤害至关重要.

研究的目的:

开发和验证二轮摩托车驾驶员事故伤害严重程度的预测模型.
识别和量化导致不同级别事故伤害严重程度的关键因素.

主要方法:

利用中国深度事故研究数据库 (CIDAS) 来获取车辆和摩托车事故数据.
对比了六种机器学习方法,选择了LightGBM,因为它在预测受伤严重程度方面具有卓越的性能.
采用SHAP (夏普利添加式解释) 方法用于模型解释性和因子分析.

主要成果:

轻GBM模型实现了高预测准确性 (92.6%),F1-Score (92.8%) 和AUC (0.986).
影响受伤严重性的关键因素包括驾驶员的年里程,摩托车手的投距离和道路速度限制.
超过1000厘米的投距离显著增加了致命伤害的可能性.

结论:

轻GBM和SHAP模型组合有效预测摩托车手受伤的严重程度,并确定关键影响因素.
调查结果为交通管理当局提供了宝贵的见解,以实施有针对性的安全措施.
该研究有助于改善弱势摩托车驾驶者的道路安全.

关键词:

轻GBMM 轻GBM 轻GBM 轻GBM 这就是 SHAP SHAP 的意思.交通安全交通安全交通安全撞车事故严重程度的严重性机器学习是机器学习.摩托车事故摩托车事故

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