一个嵌套的分组随机参数负二项式模型用于模拟分段级崩计数
1Civil Engineering Department, College of Engineering, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Heliyon
|April 10, 2024
概括
一个新的统计模型准确地预测了道路上的交通事故数量. 它显示,随着时间的推移,事故率下降,受道路长度和肩宽度的影响,有助于提高安全性.
科学领域:
- 运输工程 运输工程
- 统计建模 统计建模
- 道路安全分析 道路安全分析
背景情况:
- 道路安全分析需要准确的碰撞预测模型.
- 现有的模型可能无法完全捕捉崩数据在时间和地理单元之间的复杂相关性.
- 纵向数据分析对于了解交通事故的时间趋势至关重要.
研究的目的:
- 提出和验证一种新的嵌套组合随机参数负二项式框架,用于建模碰撞计数.
- 为了考虑沿县路线和随着时间的推移而发生的撞车数据的相关性.
- 为了分析2012年至2017年俄俄州未分割的两车道动脉道路上的撞车数趋势.
主要方法:
- 开发一个包含随机参数的三级纵向框架.
- 该模型应用于俄俄州未分割的双车道动脉道路的撞车数据.
- 与固定和可变斜率的模型变体进行比较,以评估合适度.
主要成果:
- 观察到时间和碰撞数量之间存在显著的二次关系,这表明增加率正在下降.
- 17%的段落和2%的路线显示,随着时间的推移,事故数量以加速的速度减少.
- 分段长度与碰撞有积极的相关性,而肩膀宽度总量显示出负相关性,路线之间存在显著的差异.
结论:
- 拟议的嵌套组合随机参数负二项式模型为崩计数提供了高预测准确度.
- 该模型为道路安全的时间趋势和空间变化提供了有价值的见解.
- 这一框架是道路安全改进战略中数据驱动决策的强有力的工具.
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