Wyoming Technology Transfer Center, Laramie, WY, USA.
Int J Inj Contr Saf Promot. 2020 Jun;27(2):232-242. doi: 10.1080/17457300.2020.1734943. Epub 2020 Mar 9.
The severity of traffic barrier in the literature has been modelled considering different factors including human, environmental and road/traffic barrier characteristics. However, all these factors are interacting in a complicated way, and a real relationship between these factors is still unclear. A structural equation modelling (SEM) can be adopted to capture the intricate relationships between the contributory factors and latent (unseen) factors. This study was conducted by adopting multi-group SEM to unlock the complicated relationship between confounding factors and traffic barrier crash severity by considering differences across two important groups. Due to the possible difference across different highway systems, multi-group SEM was used instead of standard SEM to account for the differences across highway and interstate roadway system. SEM is a combination of confirmatory and path analysis, which could examine relationship between different observed and latent factors. Besides using factor analysis for identification of latent factors, item/variable cluster analysis was conducted to identify all the latent factors. Although cluster analysis often has been used in other fields, this is the first time this method has been applied in transportation problems for SEM modeling. The inclusion of the factors identified by cluster analysis show an improvement in goodness of fit. This study was conducted to evaluate the traffic barrier crash severity in terms of death, injury and severity of crashes. It examined the nature and causes of severe traffic barrier in Wyoming. The results indicated that different factors contribute to the severity size of traffic barrier crashes including different traffic barrier types, demographic characteristics, weather conditions, and indirect impact of force direction. The results indicated that collision force is a latent factor with highest impact on crash severity compared with other latent factors. Different models with different number of latent were compared based on different goodness-of-fit indices and a best model, with an acceptable model fit, was selected between them. A more discussion about the model presented in the manuscript.
文献中已针对不同因素（包括人为因素、环境因素和道路/交通障碍特征）对交通障碍的严重程度进行建模。然而，所有这些因素都以复杂的方式相互作用，这些因素之间的真实关系仍不清楚。结构方程模型（SEM）可用于捕捉促成因素与潜在（未观察到）因素之间的复杂关系。本研究采用多群组 SEM 来解锁混杂因素与交通障碍碰撞严重程度之间的复杂关系，同时考虑到两个重要群组之间的差异。由于不同高速公路系统之间可能存在差异，因此使用多群组 SEM 而不是标准 SEM 来考虑高速公路和州际道路系统之间的差异。SEM 是验证分析和路径分析的结合，可以检验不同观察因素和潜在因素之间的关系。除了使用因子分析来识别潜在因素外，还进行了项目/变量聚类分析来识别所有潜在因素。虽然聚类分析经常用于其他领域，但这是该方法首次应用于交通问题的 SEM 建模。聚类分析所识别的因素的纳入提高了拟合优度。本研究旨在根据死亡、受伤和碰撞严重程度来评估交通障碍碰撞的严重程度。它检查了怀俄明州严重交通障碍的性质和原因。结果表明，不同因素导致交通障碍碰撞的严重程度不同，包括不同类型的交通障碍、人口统计学特征、天气条件以及力的间接影响方向。结果表明，碰撞力是对碰撞严重程度影响最大的潜在因素，与其他潜在因素相比。基于不同的拟合优度指数，对具有不同潜在因素数量的不同模型进行了比较，并在它们之间选择了一个最佳模型，该模型具有可接受的拟合度。文中对提出的模型进行了更详细的讨论。
