Transportation Research Record: Journal of the Transportation Research Board ( IF 1.7 ) Pub Date : 2021-04-28 , DOI: 10.1177/03611981211009880 Muhammad Tahmidul Haq 1 , Milan Zlatkovic 2 , Khaled Ksaibati 2
The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.
中文翻译:
驾驶行为对商用卡车驾驶员伤害严重性的评估
分解建模方法是分析卡车撞车事故严重程度的文献中的新趋势。文献中仍然缺少基于驾驶员动作对卡车驾驶员伤害严重性的评估。本文提供了广泛的探索性分析,根据不同的动作类型(例如,激进驾驶,无法保持正确的车道,过快驾驶以及没有不当驾驶),突出了卡车司机受伤严重程度的显着差异。利用怀俄明州10年(2007年至2016年)的历史碰撞数据,开发了使用贝叶斯随机拦截方法的二元logistic回归分析方法,以检查造成卡车司机致命或任何伤害的因素。进行了对数似然比测试,以证明需要保证采用各种驾驶行为类型的单独模型。结果表明,各种车辆,驾驶员,碰撞和道路特性以及卡车驾驶员特有的动作对相应驾驶员伤害严重性的影响。在各种驾驶相关的撞车事故中,发现车辆总重,驾驶员的年龄和性别,一天中的时间,照明条件以及路口的出现对卡车驾驶员伤害的严重程度具有显着不同的影响。通过在建模过程中加入随机拦截,分析发现同一碰撞中驾驶员伤害严重性与碰撞内部相关性的相关性很强(27%–33%)。最后,根据这项研究的结果,提出了一些建议。相应的驾驶员伤害严重程度。在各种驾驶相关的撞车事故中,发现车辆总重,驾驶员的年龄和性别,一天中的时间,照明条件以及路口的出现对卡车驾驶员伤害的严重程度具有显着不同的影响。通过在建模过程中加入随机拦截,分析发现同一碰撞中驾驶员伤害严重性与碰撞内部相关性的相关性很强(27%–33%)。最后,根据这项研究的结果,提出了一些建议。相应的驾驶员伤害严重程度。在各种驾驶相关的撞车事故中,发现车辆总重,驾驶员的年龄和性别,一天中的时间,照明条件以及路口的出现对卡车驾驶员伤害的严重程度具有显着不同的影响。通过在建模过程中加入随机拦截,分析发现同一碰撞中驾驶员伤害严重性与碰撞内部相关性的相关性很强(27%–33%)。最后,根据这项研究的结果,提出了一些建议。在各种驾驶行为相关的撞车事故中,路口的存在对卡车司机伤害的严重程度影响显着不同。通过在建模过程中加入随机拦截,分析发现同一碰撞中驾驶员伤害严重性与碰撞内部相关性的相关性很强(27%–33%)。最后,根据这项研究的结果,提出了一些建议。在各种驾驶行为相关的撞车事故中,路口的存在对卡车司机伤害的严重程度影响显着不同。通过在建模过程中加入随机拦截,分析发现同一碰撞中驾驶员伤害严重性与碰撞内部相关性的相关性很强(27%–33%)。最后,根据这项研究的结果,提出了一些建议。