A sequel to “Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama”: Accounting for temporal instability in crash factors

IF 5.7 1区 工程技术 Q1 ERGONOMICS Accident; analysis and prevention Pub Date : 2024-07-29 DOI:10.1016/j.aap.2024.107723
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Abstract

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.

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阿拉巴马州城乡道路单车和多车大型卡车过失碰撞事故综合分析 "续篇:考虑碰撞因素的时间不稳定性。
本探索性研究是 2014 年研究的后续研究,该研究调查了阿拉巴马州大型卡车过失碰撞结果的相关因素。为了评估碰撞因素影响中未观察到的时间变化,本研究使用 2017 年至 2019 年的碰撞数据重新创建了 2014 年研究中开发的原始碰撞模型。使用前一项研究中使用的相同变量重新创建了四个混合对数模型,以分析城市和农村环境中单车(SV)和多车参与(MV)大型卡车过失碰撞事故中造成伤害严重程度的碰撞因素。研究发现,许多因素对碰撞严重程度的影响随着时间的推移发生了变化,其中一些因素与碰撞结果的关系不再显著,而另一些因素则仍然显著。此外,研究还发现,在较新的严重程度模型中,一些仍然重要的变量与碰撞伤害严重程度的关系有所不同。例如,疲劳驾驶(在农村碰撞事故中)、晴朗天气(在城市碰撞事故中)、单车卡车(在农村 SV 碰撞事故中)、卡车侧翻(在城市 SV 碰撞事故中)等因素随着时间的推移保持了一致的显著性,而有过失的男性驾驶员(在城市中型客货车碰撞事故中)、有过失的女性驾驶员(在城市中型客货车碰撞事故中)和撞到固定物体(在农村中型客货车碰撞事故中)等变量的影响却发生了变化。其中一个明显的差异是交通管制缺失变量,在 2014 年的模型中,该变量使农村 SV 碰撞中的重大伤害概率增加了 49.50%,但使用 2017-2019 年的数据,该变量使记录重大伤害的概率降低了 108.90%。考虑到在重新创建的模型中观察到的时间变化,开发了更新的模型,揭示了新变量的出现,如与卡车碰撞严重性显著相关的卡车年龄。这项研究的结果提供了证据,表明大型卡车过失碰撞事故的某些碰撞严重性因素会随着时间的推移而变化,而更新的因素也会随着时间的推移而出现。这些发现也有助于卡车公司、交通工程师和其他行业专家制定减少大型卡车碰撞事故的措施。
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来源期刊
CiteScore
11.90
自引率
16.90%
发文量
264
审稿时长
48 days
期刊介绍: Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.
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