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阿拉巴马州涉及本州和外州大型卡车司机的撞车事故严重程度分析:均值和方差具有异质性的随机参数多项logit模型

Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances.

作者信息

Okafor Sunday, Adanu Emmanuel Kofi, Jones Steven

机构信息

Department of Civil, Construction, and Environmental Engineering, The University of Alabama, USA.

Alabama Transportation Institute, USA.

出版信息

Heliyon. 2022 Nov 30;8(12):e11989. doi: 10.1016/j.heliyon.2022.e11989. eCollection 2022 Dec.

Abstract

The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016-2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-of-state large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-of-state model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers' training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.

摘要

货运行业对美国的经济活力贡献巨大。大型卡车主要用于州内和跨州运输货物。尽管其具有经济重要性,但大型卡车碰撞事故引发了公共安全担忧。为了将后果降至最低,有必要了解导致涉及卡车碰撞事故严重后果的因素。由于许多大型卡车司机跨多个州运输货物,预计以司机为中心的碰撞因素在州内和州外司机之间会有所不同。因此,本研究开发了两个均值和方差具有异质性的随机参数多项logit模型,以检验导致阿拉巴马州涉及州内和州外大型卡车司机碰撞事故严重程度的因素。该研究基于2016 - 2020年阿拉巴马州的大型卡车碰撞事故。在数据清理和准备之后,发现约20%的州内大型卡车碰撞事故和23%的州外大型卡车碰撞事故与疲劳有关。州内大型卡车司机中与超速相关的碰撞事故更多(12.4%),但超速对碰撞严重后果的影响仅在州外模型中显著。在州外司机中观察到更多与闯红灯违规相关的碰撞事故(14.2%),这表明存在疲劳以及对阿拉巴马州信号控制交叉口运行不熟悉的根本问题。该研究通过揭示涉及州内和州外大型卡车司机碰撞事故的细微差别,为大型卡车碰撞事故的文献做出了贡献。尽管影响碰撞结果的因素看似相似,但本研究为卡车司机培训以及关于各州道路特征可能存在差异的宣传活动提供了依据。此外,应探索优先考虑大型卡车司机福祉并为他们创造更好工作条件的政策制定和策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de71/9720575/375d96c0b676/gr1.jpg

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