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Alejandro Moreno-Sanfélix^1,2, F Consuelo Gragera-Peña³, Miguel A Jaramillo-Morán³

¹Universidad de Extremadura, Escuela de Ingenierías Industriales, Av. Elvas, s/n, Badajoz, 06006, Spain. almorenos@alumnos.unex.es.

Scientific reports

|March 4, 2026

概括

机器学习模型,特别是决策树 (DT),可以客观地确定行人交通事故中的责任. 司机司机司机司机

科学领域:

道路安全研究道路安全研究
交通事故分析分析
机器学习应用程序机器学习应用程序

背景情况:

步行者交通事故造成严重伤亡.
准确的责任分配对于法律和政策决策至关重要.
有限的研究存在于行人碰撞的责任.

研究的目的:

评估监督机器学习 (ML) 模型,用于对驾驶员和行人在碰撞中的责任进行分类.
确定影响责任归因的关键变量.
支持司法和警察当局在客观的决策.

主要方法:

利用来自西班牙警方和司法报告的510起行人撞车事故数据集.
评估监督分类模型,包括决策树 (DT),天真湾 (NB) 和支持矢量机 (SVM).
在人类,技术,结构和规范子系统中分析了14个二进制变量.

主要成果:

与原始湾 (NB) 和支持向量机器 (SVM) 相比,决策树 (DT) 显示出更高的性能.
持有驾驶执照是决定责任的最有影响的因素 (47.26%).
其他重要因素包括行人位置 (15.35%),驾驶员障碍 (7.24%) 和分心驾驶 (7.04%).

结论:

机器学习,特别是DT,提供了一种有效的工具,用于在行人撞车中客观地归咎责任.
识别关键影响因素可以为道路安全战略和政策制定提供信息.
这种方法提高了司法和警察当局的效率和客观性.

关键词:

机器学习机器学习步行者通道步行者通道责任感责任感责任感交通事故发生的原因

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