This study examines contributing variables affecting the driver injury-severity in single-vehicle roadway departure crashes. To capture the threshold heterogeneity and unobserved heterogeneity, the random thresholds random parameters hierarchical ordered probit (HOPIT) approach is employed using the crash data collected from 2014 to 2017 in the State of North Carolina. Three injury severity levels are considered: severe injury, minor injury, and no injury. Attributes that potentially affect crash severity are examined, including driver, crash, roadway, and environmental characteristics. A series of likelihood ratio tests are conducted to examine the temporal stability of factors across different studied years. The marginal effects of factors in different injury-severity models are compared. Significant temporal instability is found among the studied year periods. The threshold value estimated using the random threshold random parameters HOPIT model is found to be random parameters and determined by specific explanatory variables. Additionally, the effects of some factors (e.g., alcohol, curved roadway, passenger car, SUV, and wet/water surface) on injury severity are relatively stable, while others (e.g., female driver, collector, and clear weather) present temporal unstable effects. Regarding the temporal instability, decision-makers need to treat the factors carefully to avoid developing incorrect countermeasures.

这项研究研究了影响单车道路偏离事故中驾驶员伤害严重性的因素。为了捕获阈值异质性和未观察到的异质性，使用了从北卡罗来纳州2014年至2017年收集的崩溃数据的随机阈值随机参数分层有序概率（HOPIT）方法。考虑了三种伤害严重程度：严重伤害，轻度伤害和无伤害。检查了可能影响碰撞严重性的属性，包括驾驶员，碰撞，道路和环境特征。进行了一系列似然比检验，以检验不同研究年份中因素的时间稳定性。比较了不同伤害严重度模型中因素的边际效应。在所研究的年份中发现了明显的时间不稳定。发现使用随机阈值随机参数HOPIT模型估算的阈值是随机参数，并由特定的解释变量确定。此外，某些因素（例如，酒精，弯曲的道路，乘用车，SUV和湿/水表面）对伤害严重性的影响相对稳定，而其他一些因素（例如，女驾驶员，收割者和晴朗的天气）则表现为时间不稳定效果。关于时间上的不稳定性，决策者需要谨慎对待这些因素，以避免制定出错误的对策。某些因素（例如，酒精，弯曲的道路，乘用车，SUV和湿/水表面）对伤害严重性的影响相对稳定，而其他因素（例如，女性驾驶员，收割者和晴朗的天气）则表现出暂时的不稳定影响。关于时间上的不稳定性，决策者需要谨慎对待这些因素，以避免制定出错误的对策。某些因素（例如，酒精，弯曲的道路，乘用车，SUV和湿/水表面）对伤害严重性的影响相对稳定，而其他因素（例如，女性驾驶员，收割者和晴朗的天气）则表现出暂时的不稳定影响。关于时间上的不稳定性，决策者需要谨慎对待这些因素，以避免制定出错误的对策。