Road traffic crashes remain a major concern globally resulting in loss of life and worsening the quality of life and productivity of the crash survivors. The current study contributes to road safety literature by focusing on developing high resolution crash severity models based on driver injury severity reported using Abbreviated Injury Scale (AIS) by body region. For this purpose, the research develops a joint random parameters multivariate model structure with as many dimensions as severity by body location. The proposed model system is developed using Crash Injury Research Engineering Network (CIREN) data, which includes patients admitted to trauma centers due to a crash from 2005 to 2015. The dataset contained information about a comprehensive set of exogenous variables including driver characteristics, vehicle characteristics, crash characteristics, roadway characteristics, and environmental characteristics. The empirical analysis involves the estimation of Random Parameters Multivariate Generalized Ordered Probit Model that allows for the influence of common unobserved factors affecting the vehicle occupant severity across body locations. The model estimation results are further augmented by conducting elasticity analysis to highlight the differential impact of various factors on severity across body regions.

道路交通撞车仍然是全球关注的主要问题，导致生命损失，撞车幸存者的生活质量和生产率不断下降。当前的研究通过基于根据身体部位使用缩写伤害量表（AIS）报告的驾驶员伤害严重性，着重开发高分辨率的碰撞严重性模型，为道路安全文献做出了贡献。为此，研究开发了一种联合随机参数多元模型结构，其结构与根据身体位置的严重程度一样多。拟议的模型系统是使用碰撞伤害研究工程网络（CIREN）数据开发的，该数据包括2005年至2015年因碰撞而入住创伤中心的患者。数据集包含有关一组全面的外生变量的信息，包括驾驶员特征，车辆特征，碰撞特征，巷道特征和环境特征。实证分析涉及对随机参数多元广义有序概率模型的估计，该模型允许影响整个身体位置车辆乘员严重性的常见未观察因素的影响。通过进行弹性分析以突出各种因素对整个身体部位严重程度的不同影响，可以进一步提高模型估计结果。