Department of Civil, Construction, and Environmental Engineering, The University of Alabama Tuscaloosa, AL 35487-0205, United States.
Alabama Transportation Institute, The University of Alabama Tuscaloosa, AL 35487-0205, United States.
Accid Anal Prev. 2024 Oct;206:107723. doi: 10.1016/j.aap.2024.107723. Epub 2024 Jul 29.
This exploratory study is a follow-up to a 2014 study that investigated factors associated with large truck at-fault crash outcomes in Alabama. To assess unobserved temporal changes in the effects of the crash factors, this study re-creates the original crash models developed in the 2014 study using crash data from 2017 to 2019. Four mixed logit models were re-created using the same variables used in the previous study to analyze contributing crash factors to injury severity of single-vehicle (SV) and multi-vehicle-involved (MV) large truck at-fault crashes in urban and rural settings. It was found that there have been temporal changes in how many of the factors influenced crash severity with some of them no longer showing any significant association with crash outcomes, while others remained significant. Further, it was observed that some of the variables that remained significant had different relationships with crash injury severity in the newer severity models. For instance, while factors such as fatigued driver (in rural crashes), clear weather (in urban crashes), single-unit truck (in rural SV crashes), truck rollover (in urban SV crashes) maintained consistent significance over time, the effects of variables such as at-fault male drivers (in urban MV crashes), at-fault female drivers (in urban MV crashes), and hitting fixed object (in rural MV crashes) have changed. One such notable difference is the variable for absence of traffic control which increased the probability of major injury in rural SV crashes by 49.50% in the 2014 model but decreased the probability of recording major injuries by 108.90% using the 2017-2019 data. Considering the temporal changes that were observed in the recreated models, newer models were developed, revealing the emergence of new variables such as truck age that are significantly associated with truck crash severity. The findings of this study provide evidence to suggest that some crash severity factors for at-fault large truck collisions vary over time, with newer ones also emerging over time. These findings can also help trucking companies, transportation engineers, and other industry experts in developing measures to reduce large truck crashes.
本探索性研究是对 2014 年一项研究的后续调查,该研究调查了阿拉巴马州与大卡车事故责任相关的因素。为了评估事故因素未被观察到的时间变化,本研究使用 2017 年至 2019 年的事故数据,重新创建了 2014 年研究中开发的原始事故模型。使用之前研究中使用的相同变量,重新创建了四个混合逻辑回归模型,以分析城市和农村环境中单辆(SV)和多辆涉事(MV)大卡车事故责任中导致受伤严重程度的因素。结果发现,有一些因素对事故严重程度的影响随着时间的推移而发生了变化,其中一些因素不再与事故结果有任何显著关联,而另一些因素仍然显著。此外,还观察到,在新的严重程度模型中,一些仍然显著的变量与事故伤害严重程度的关系有所不同。例如,虽然疲劳司机(在农村事故中)、晴朗天气(在城市事故中)、单辆卡车(在农村 SV 事故中)、卡车翻车(在城市 SV 事故中)等因素随着时间的推移保持一致的显著地位,但变量的影响,如事故中男性司机(在城市 MV 事故中)、事故中女性司机(在城市 MV 事故中)和撞击固定物体(在农村 MV 事故中),已经发生了变化。一个值得注意的差异是,没有交通管制的变量增加了农村 SV 事故中重伤的概率,在 2014 年的模型中增加了 49.50%,但在使用 2017-2019 年的数据时,重伤的概率降低了 108.90%。考虑到重新创建的模型中观察到的时间变化,开发了新的模型,揭示了新的变量的出现,例如与卡车碰撞严重程度显著相关的卡车年龄。本研究的结果提供了证据,表明一些与大卡车事故责任相关的因素随着时间的推移而变化,而且随着时间的推移,也出现了新的因素。这些发现还可以帮助卡车运输公司、交通工程师和其他行业专家制定减少大卡车事故的措施。