使用基于多源数据的分类和回归树对高速公路事故持续时间的调查
1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
分类和回归树 (CART) 使用高速公路传感器数据准确预测事故持续时间. 这种机器学习方法提高了应急规划和事件管理的有效性.
科学领域:
- 交通工程是交通工程.
- 数据挖掘 数据挖掘
- 机器学习 机器学习
背景情况:
- 在高速公路上发生的事件会造成重大损害和延误.
- 有针对性的应急措施有效地减轻了事件的影响.
- 预测事件持续时间对于有效应急响应至关重要.
研究的目的:
- 使用分类和回归树 (CART) 数据挖掘技术预测和量化事件持续时间.
- 将CART的预测准确性和可解释性与其他机器学习模型进行比较.
- 制定数据驱动的规则,以进行有效的事件评估和应急规划.
主要方法:
- 利用来自杭州高速公路 (2019-2021) 的多传感器数据.
- 使用CART.构建了一个八层,14叶节点回归树.
- 从CART模型中提取了14条规则,以告知应急措施.
- 将CART与XGBoost,随机森林 (RF) 和加速失效时间 (AFT) 模型进行比较.
主要成果:
- 与XGBoost,RF和AFT相比,CART模型显示出更高的预测准确度.
- 该CART方法提供了强大的可解释性,捕获多达七个变量之间的相互作用.
- 衍生规则促进了准确的事件评估和有效的应急计划实施.
结论:
- CART是一种高效和可解释的方法,用于预测高速公路上的事故持续时间.
- 该研究的结果支持机器学习在交通管理中的工程应用.
- 来自CART的数据驱动洞察力可以改善事件响应并减少整体伤害.
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