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Weekly variations and temporal instability of determinants influencing alcohol-impaired driving crashes: A random thresholds random parameters hierarchical ordered probit model
Analytic Methods in Accident Research ( IF 12.9 ) Pub Date : 2021-09-23 , DOI: 10.1016/j.amar.2021.100189
Xintong Yan 1 , Jie He 1 , Guanhe Wu 2 , Changjian Zhang 1 , Ziyang Liu 1 , Chenwei Wang 1
Affiliation  

Alcohol consumption has been acknowledged as a critical determinant concerning the occurrence of vehicle crashes and their resulting injury severities. To investigate the weekly transferability and temporal stability of the contributors determining different injury severity levels in alcohol-impaired driving crashes, this paper employs two groups of random thresholds random parameters hierarchical ordered probit models. Three injury-severity categories are determined as outcome variables: no injury, minor injury and severe injury, while multiple factors are investigated as explanatory variables including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics and temporal characteristics. The weekly transferability and temporal stability of the models are examined through three groups of likelihood ratio tests. Marginal effects are also adopted to analyze the weekly transferability and temporal stability of the explanatory variables. Overall, the findings exhibit weekly variations and temporal instability while some indicators are also observed to be of relative weekly transferability including speeding, aggressive driving, proceeding, motorcycle, speed limit between 45 and 55 mph, curve, driveway, overturned, hit-fixed-object, vehicle age (5–9 years old). Besides, curve and passenger car indicators in weekday models present relative temporal stability. This paper can provide insights into preventing alcohol-impaired driving crashes and can potentially facilitate the development of the corresponding crash injury mitigation policies. More studies could be conducted integrating the advanced data-driven methods into the statistical models to simultaneously achieve inference and prediction.



中文翻译:

影响酒后驾驶碰撞的决定因素的每周变化和时间不稳定性:随机阈值随机参数分层有序概率模型

饮酒已被公认为是关于车辆碰撞发生及其造成的伤害严重程度的关键决定因素。为了研究酒后驾驶碰撞中确定不同伤害严重程度的因素的每周可转移性和时间稳定性,本文采用了两组随机阈值随机参数分层有序概率模型。三个伤害严重性类别被确定为结果变量:无伤害、轻微伤害和严重伤害,同时调查了多个因素作为解释变量,包括驾驶员特征、车辆特征、道路特征、环境特征、碰撞特征和时间特征。通过三组似然比检验检查模型的每周可转移性和时间稳定性。还采用边际效应来分析解释变量的每周可转移性和时间稳定性。总体而言,调查结果显示出每周变化和时间不稳定性,同时还观察到一些指标具有相对每周可转移性,包括超速、激进驾驶、前进、摩托车、速度限制在 45 到 55 英里/小时之间、弯道、车道、翻车、命中固定-物体,车龄(5-9 岁)。此外,平日模型中的曲线和乘用车指标呈现相对的时间稳定性。本文可以提供有关预防酒后驾驶碰撞的见解,并可能有助于制定相应的碰撞伤害减轻政策。可以进行更多的研究,将先进的数据驱动方法集成到统计模型中,以同时实现推理和预测。

更新日期:2021-10-02
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