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用机器学习模型预测印度高速公路交通事故严重性的方法框架.

Humera Khanum1,2, Anshul Garg2, Mir Iqbal Faheem3

  • 1Civil Engineering, Symbiosis Institute of Technology, Symbiosis Knowledge Village, Near Lupin Research Park, Gram Lavale, Mulshi, Pune, 412115, Maharashtra, India.

MethodsX
|December 11, 2025
PubMed
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这项研究使用机器学习来预测印度的道路事故严重程度,确定车辆类型和道路状况等关键因素. 该SHAP增强模型为改善道路安全措施提供了可解释的见解.

科学领域:

  • 道路交通安全问题 道路安全问题
  • 机器学习应用程序 机器学习应用程序
  • 交通事故分析分析

背景情况:

  • 道路交通事故构成了全球重大威胁,印度每年有超过15万人死亡.
  • 现有的模型很难准确地表示事故风险因素的复杂相互作用.
  • 需要先进的分析框架来理解和减轻事故严重程度.

研究的目的:

  • 开发和评估用于预测印度道路交通事故严重性的机器学习模型.
  • 为了提高模型的可解释性,使用SHAP值来识别关键影响因素.
  • 为改善高速公路安全和告知政策提供数据驱动的框架.

主要方法:

  • 实施随机森林和梯度提升算法用于严重性预测.
  • 应用SHAP (SHapley添加式扩展) 值来确定特征的重要性.
  • 使用准确性,精度,回忆和F1分数等标准指标评估模型性能.

主要成果:

  • 车辆类型,事故地点和道路状况被确定为事故严重性的重要预测因素.
  • 用SHAP增强的模型提供了对每个因素贡献的清晰洞察力.
  • 模型性能指标证明了拟议框架的有效性.
关键词:
梯度增强模型的模型.印度高速公路 印度高速公路机器学习模型的机器学习模型随机森林模型是一个随机森林模型.预测道路交通事故严重程度的预测.

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结论:

  • 通过SHAP增强的机器学习方法提供了一种可靠和可解释的方法来预测道路交通事故的严重程度.
  • 调查结果为印度制定有针对性的道路安全干预提供了可操作的见解.
  • 该框架支持主动安全措施和高速公路基础设施改进.