在县和非县道路上使用可解释表式深度学习 (TabNet) 评估农业设备车辆在县和非县道路上的碰撞风险因素
Md Monzurul Islam1, Jinli Liu1, Rohit Chakraborty1
1Texas State University, 601 University Drive, San Marcos, TX 78666, USA.
Accident; analysis and prevention
|April 19, 2025
概括
农业设备车辆撞车在公共道路上构成风险. 县道路显示与速度限制和人口统计相关的严重程度更高,而非县道路受到照明和交通复杂性的影响.
科学领域:
- 交通安全研究 交通安全研究
- 农业工程 农业工程
- 数据科学在运输中的应用.
背景情况:
- 由于速度差异,农业设备车辆撞车在公共道路上带来了独特的安全挑战.
- 了解影响碰撞严重性的因素对于制定有针对性的安全干预至关重要.
研究的目的:
- 为了分析涉及农业设备车辆的撞车数据.
- 为了比较影响县道路与非县道路撞车严重性的因素.
- 确定影响农业设备车辆事故结果的关键变量.
主要方法:
- 利用农业设备车辆撞车事故的数据集.
- 应用合成少数人过量采样技术 (SMOTE) 用于数据平衡.
- 采用TabNet深度学习模型进行崩动态分析,包括特征重要性和SHAP图.
主要成果:
- 在县道路上,事故严重程度受限速度,首次有害事件,交通管制和人员年龄的影响.
- 在非县道路上,照明条件,十字路口特征和人口群体是重要的因素.
- 速度限制是所有道路类型和严重程度的关键因素.
结论:
- 县道和非县道之间的撞车严重性的决定因素有所不同,这凸显了定制安全策略的必要性.
- 针对不同的道路环境,建议针对可见性,速度管理和教育进行有针对性的干预.
- 来自深度学习模型的数据驱动洞察力增强了对农业设备车辆安全的理解.
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