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Updated: Jan 10, 2026

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基于SHAP的整体交通风险因果关系模式的解释,使用高分辨率轨迹数据和风险场理论
Bin Li1,2, Jun Hua1,3, Pengcheng Li1,3
1State Key Laboratory of Intelligent Transportation System, Research Institute of Highway Ministry of Transport, Beijing, China.
Traffic injury prevention
|November 25, 2025
概括
这项研究引入了一种全方位评估道路交通风险的新方法,确定了车辆类型分布等关键因素. 开发的模型准确地预测冲突的发生,改进了道路安全分析.
科学领域:
- 交通工程是交通工程.
- 道路安全分析 道路安全分析
- 机器学习应用 机器学习应用
背景情况:
- 使用高分辨率轨迹数据评估道路交通安全至关重要.
- 专注于单一冲突类型的现有方法无法捕捉整体的道路段风险.
- 需要采取全面的方法来评估整体的交通风险,并确定影响因素.
研究的目的:
- 开发一种方法来评估道路段级交通风险,无论具体冲突类型如何.
- 分析影响交通风险的关键因素并探索它们的潜在机制.
- 提高基于机器学习的交通风险模型的可解释性.
主要方法:
- 提出了一项基于风险场理论的综合代孕安全措施,用于全面识别冲突.
- 开发了路段级整体风险评估模型,使用后勤回归和五种机器学习方法.
- 应用重新抽样技术来解决交通冲突数据中不平衡的样本类别.
主要成果:
- 随机森林模型实现了80.6%的预测准确度,89.0%的精度,86.8%的冲突事件回忆.
- SHAP分析揭示了交通状态变量及其相互作用对交通风险的贡献.
- 交通流中的车辆类型分布被确定为影响冲突发生的重要因素.
结论:
- 该研究为道路段提供了准确的风险预测,超越了单一冲突类型分析.
- 机器学习模型的增强可解释性将预测与对交通风险的因果洞察联系起来.
- 这些发现有助于更深入地了解交通动态和道路安全机制.
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