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Application of Bayesian ordinal logistic model for identification of factors to traffic barrier crashes: considering roadway classification
Transportation Letters ( IF 2.8 ) Pub Date : 2020-02-12 , DOI: 10.1080/19427867.2020.1728041
Mahdi Rezapour 1 , Shaun S. Wulff 2 , Amirarsalan Mehrara Molan 3 , Khaled Ksaibati 1
Affiliation  

ABSTRACT

One of the main objectives of policymakers is to reduce crash severity due to high social impacts and economic loss associated with severe crashes. One of the most efficient ways to achieve this objective is through identification of the contributory factors to severe crashes. Highway traffic barriers have been installed with the objective of protecting motorists who have drifted off the roadway. Although these traffic barriers save many lives, the crash severity for these crashes are disproportionally high. Only traffic barriers crashes were considered in this study to identify the factors for these types of crashes. Moreover, due to the importance of low volume crashes, especially in rural areas like Wyoming, this study investigated the effects of road classification on crash severity as well as how these effects impact the role of the contributory factors. Low volume roads often receive less attention in terms of road safety due to their low crash frequencies. A Bayesian approach was used to fit the models since this approach does not require large sample assumptions, it does not rely on approximations for estimating nonlinear functions of the parameters, and also it provides simpler interpretations for model unknowns. The factors identified by this study included the main effects of day of the week, seasonality, improper restraints as well as the interaction effects of low volume roads with shoulder width, road surface conditions, and lighting conditions. These interaction terms indicated that the effects of these contributory factors change with the traffic volume. Possible causes of the significant main and interaction terms are discussed in the manuscript.



中文翻译:

贝叶斯有序逻辑模型在识别交通障碍物碰撞因素中的应用:考虑巷道分类

摘要

决策者的主要目标之一是降低由于严重撞击而造成的高社会影响和经济损失所造成的撞击严重性。实现此目标的最有效方法之一是通过识别导致严重撞车的因素。已经安装了高速公路交通障碍物,目的是保护偏离道路的驾车者。尽管这些交通障碍挽救了许多生命,但这些崩溃的崩溃严重性却过高。在本研究中,仅考虑了交通障碍事故,以识别造成这些事故类型的因素。此外,由于交通事故数量很少,尤其是在怀俄明州等农村地区,这项研究调查了道路分类对碰撞严重程度的影响以及这些影响如何影响归因因素的作用。低容量道路由于其低碰撞频率而在道路安全方面通常很少受到关注。使用贝叶斯方法来拟合模型,因为该方法不需要大的样本假设,它不依赖于近似值来估计参数的非线性函数,而且它为模型未知数提供了更简单的解释。这项研究确定的因素包括星期几的主要影响,季节性,不适当的约束以及小体积道路与路肩宽度,路面状况和光照条件的相互作用。这些交互作用术语表明,这些促成因素的影响随交通量而变化。手稿中讨论了重要的主要术语和相互作用术语的可能原因。

更新日期:2020-02-12
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