在双车道,双向农村道路上分析交通伤害严重程度的堆叠模型
Ali Tavakoli Kashani1, Parsa Soleyman Farahani1, Hamzeh Mansouri Kargar1
1Road Safety Research Center, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
概括
这项研究引入了一种两层堆叠模型来预测交通事故严重程度,其性能优于传统方法. 该模型有效地对影响受伤严重程度的因素进行分类,以更好地评估风险和干预策略.
科学领域:
- 交通安全 交通安全 交通安全
- 机器学习 机器学习
- 数据分析 数据分析
背景情况:
- 准确分析交通事故中受伤严重程度对于有效的风险评估和交通管理机构的成本效益干预至关重要.
- 现有的模型可能无法完全捕捉影响事故结果的复杂相互作用.
- 预测事故严重程度有助于优先考虑安全措施和资源分配.
研究的目的:
- 为预测交通事故伤害严重程度提出一种新的两层堆叠模型.
- 评估拟议的堆叠模型与传统分类算法的性能.
- 识别和分类不同组件对预测事故严重性的影响.
主要方法:
- 开发了一种两层堆叠模型,将随机森林,决策树,K近邻和支持向量机在第一层中集成.
- 后勤回归和随机森林算法用于第二层的最终严重程度分类.
- 模型校准涉及优化参数,如树的数量,学习率和正规化系数,使用系统的网格搜索策略.
- 这项研究分析了135条双向,双车道道路上记录的24,141起交通事故.
主要成果:
- 开发的堆叠模型在预测事故严重程度方面,与传统模型相比,表现优越.
- 该模型成功地对碰撞伤害的严重程度进行了分类,并提供了对促成因素的见解.
- 组件智能分析对各种因素对预测结果的影响进行了分类.
结论:
- 两层堆叠模型为预测交通事故伤害严重程度提供了更有效的方法.
- 这些发现可以帮助交通管理机构制定有针对性的和具有成本效益的安全干预措施.
- 这项研究为分析事故数据和改善道路安全提供了强大的框架.
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