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开发基于XGBoost的模型来预测由宏观操作和保险数据驱动的卡车撞车概率
Yiping Wu1, Hongpeng Zhang1, Peng Song1
1Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
Traffic injury prevention
|September 15, 2025
概括
一个新的卡车事故预测模型使用运营数据来识别关键原因,实现高准确度. 卡车司机熟悉和道路类型是防止商用卡车事故的重要因素.
科学领域:
- 运输安全运输安全
- 数据科学数据科学数据科学
- 预测建模预测建模
背景情况:
- 卡车事故导致严重的伤亡.
- 积极预防事故需要科学预测模型和确定主要原因.
研究的目的:
- 使用宏观操作数据开发科学卡车事故预测模型.
- 确定影响卡车事故发生的主要因素.
主要方法:
- 使用商用卡车的年度运营行为和保险索赔数据开发了一个预测模型.
- 解决了多对线性,并执行了输入维度缩小.
- 使用Shapley添加式解释 (SHAP) 值和主要组件分析 (PCA) 进行因子分析.
主要成果:
- 在卡车事故预测模型中实现了84.21%的召回率和85.33%的F1得分.
- 外部验证显示预测准确率为87.59%.
- 卡车司机熟悉道路和道路段类型被确定为重大事故影响因素.
结论:
- 建立了一个创新的,基于宏观数据的卡车事故预测模型.
- 该模型减轻了数据收集挑战,同时确保了预测准确性.
- 确定了针对性事故预防策略的关键因素.
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