一个完整的贝叶斯多层次方法,用于模拟单车车祸中的交互效应
Zhenggan Cai1, Fulu Wei2, Yongqing Guo2
1ITS Research Center, Wuhan University of Technology, Wuhan, PR China; School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, PR China.
Accident; analysis and prevention
|October 2, 2023
概括
这项研究引入了一种新的贝叶斯空间时间相互作用多层逻辑 (STIML-logit) 模型来分析单车 (SV) 碰撞严重性. 该模型有效地捕捉了复杂的相互作用和异质性,改善了碰撞伤害预测.
科学领域:
- 交通安全 交通安全
- 统计建模 统计建模
- 运输工程 运输工程
背景情况:
- 单车 (SV) 碰撞严重性建模通常忽略了关键的相互作用效应.
- 时空相互作用和因子相互作用是复杂的属性,很少被系统地解决.
研究的目的:
- 开发和验证贝叶斯的时空交互多层次逻辑 (STIML-logit) 方法,使用平均值和方差 (HMV) 的异质性.
- 系统地发现SV碰撞严重程度建模中的相互作用效应.
- 调查交通环境和个别事故因素对伤害严重性的影响.
主要方法:
- 设计了一个完整的贝叶斯式STIML-logit方法,其中含有平均值和方差 (HMV) 的异质性.
- 为时空相互作用提出了一个嵌套的高斯条件自回归 (CAR) 结构.
- 对来自中国山东96条城市道路的SV撞车数据进行了回归建模,比较了不同的模型规格.
主要成果:
- 与标准和时空模型相比,使用HMV的STIML-logit模型表现出优越的回归性能.
- 结合嵌套CAR结构的崩模型优于传统CAR结构的崩模型.
- 确定了重要的跨层次因素相互作用,表明环境因素对碰撞伤害的影响与具体案例因素有所不同.
结论:
- 系统地解决相互作用效应和平均值和差异的异质性对于准确的SV撞击严重性建模至关重要.
- 拟议的嵌套CAR结构为模拟碰撞数据中的时空相互作用提供了优势.
- 这些发现强调了交通环境因素对事故伤害严重性的动态影响,这取决于个别事故情况.
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