Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

由于在错误驾驶（WWD）事故中死亡和重伤的比例很高，因此许多研究都集中在确定导致WWD事故发生的因素上。然而，有限的研究工作已经调查了与WWD撞车事故中驾驶员伤害严重程度相关的因素。这项研究旨在使用均值和方差方法中的异质性来弥补数据集中未观察到的异质性的随机参数logit模型来弥合差距。使用了2014年至2017年在北卡罗来纳州收集的警方报告的坠机数据。定义了四个伤害严重度级别：致命伤害，严重伤害，可能伤害和无伤害。使用解释变量，包括驾驶员特征，巷道特征，环境特征和碰撞特征。估计结果表明，酒精，农村地区，主要动脉，高速度限制（> 60 mph），昏暗的条件，偏僻的道路碰撞和正面碰撞等因素，都大大增加了严重程度在WWD中崩溃。根据调查结果设计并建议了一些政策含义。