University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States.
Accid Anal Prev. 2021 Jan;149:105855. doi: 10.1016/j.aap.2020.105855. Epub 2020 Nov 24.
Traditional hierarchical modeling has been proposed to account for unobserved heterogeneity in the crash analysis. Previous studies investigated the grouping of individual observations between different clusters by considering a single random factor at level-2 of data structure. This approach, however, hinders exploring the possible crossed effects of additional random factors at the level-2 of data hierarchy on the response variable. The current study aims to expand the previous attempts by introducing the concept of Cross-Classified Random Effects Modeling (CCREM) and utilizing crossed random intercepts to account for the crossed effects of two random factors. Aligned with the Connected Vehicle Pilot Deployment Program on Interstate-80 (I-80), this paper intends to cluster critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile I-80 in Wyoming during the first five months of 2017. Aggregated environmental conditions were conflated with disaggregated real-time traffic observations. Concerning road surface conditions and longitudinal grade categories, four Logistic Regression models were calibrated under Bayesian Inference. Model-1 considered these two factors as fixed parameters; however, in each of Model-2 and Model-3, one of these factors was treated as a random intercept. Model-4 considered both factors as random intercepts and investigated their crossed effect on the critical crash probability. Model-4 outperformed the others and showed that the maximum probability of critical crashes arises on dry pavements and steep downgrades. In contrast, the combined effect of wet pavements and less steep downgrades is associated with the minimum risk of critical crashes. It was revealed that the probability of critical crashes varies at any given value of real-time traffic-related predictors according to different combinations of longitudinal grade and road surface conditions. This finding indicates an essential need for Active Traffic Management to timely apply interventions not only based on real-time traffic-related predictors but also according to various combinations of environmental conditions.
传统的层次模型已被提出，以解释在崩溃分析中的未观察到的异质性。以前的研究通过在数据结构的第 2 级考虑单个随机因素，研究了个体观测值在不同聚类之间的分组。然而，这种方法阻碍了探索数据层次结构第 2 级上的额外随机因素对响应变量的可能交叉影响。本研究旨在通过引入交叉分类随机效应模型（CCREM）的概念，并利用交叉随机截距来解释两个随机因素的交叉效应，来扩展以前的尝试。本研究与州际 80 号公路（I-80）的联网车辆试点部署计划相吻合，旨在通过在怀俄明州 2017 年前五个月内的 I-80 的 402 英里处聚类严重碰撞，涉及致命或使人丧失能力的伤害，与非严重碰撞。聚合的环境条件与分散的实时交通观测数据相结合。关于路面状况和纵向坡度类别，在贝叶斯推断下校准了四个逻辑回归模型。模型 1 将这两个因素视为固定参数；然而，在模型 2 和模型 3 中的每一个中，这些因素中的一个被视为随机截距。模型 4 将这两个因素视为随机截距，并研究了它们对严重碰撞概率的交叉影响。模型 4表现优于其他模型，并表明严重碰撞的最大概率出现在干燥路面和陡峭的下坡段。相比之下，湿路面和坡度较小的下坡段的组合效应与严重碰撞的最小风险相关。结果表明，根据纵向坡度和路面状况的不同组合，在任何给定的实时交通相关预测因子值下，严重碰撞的概率都有所不同。这一发现表明，主动交通管理迫切需要不仅根据实时交通相关预测因子，而且还根据环境条件的各种组合来及时应用干预措施。
