This study aims to rank high crash risk areas and measure the impacts of accessibility and other spatial and socio-economic factors on county-level crash severities while controlling for spatial and temporal unobserved effects. Among other insights, the results suggest that primary roadway density is positively associated with all crash severities. Contrary, intersection density is negatively associated with injury and no injury crashes. Furthermore, school density and rates of driving under the influence are both positively associated with no injury crashes. Additionally, the mean travel time to the hospital is positively associated, while urbanity is negatively associated with fatal crashes. Unobserved effects due to unstructured random effect terms are significant and contribute to the model’s overall goodness of fit. The within-area correlation among crash severities is positive and non-negligible. From a temporal perspective, both fatal and no injury crashes have decreasing trends. However, no clear pattern for injury crashes is observed across the years. From a spatial perspective, extreme high and low crash risk counties are identified throughout the study area with no clear observed patterns of clustering or dispersion.

这项研究旨在对高事故风险区域进行排名，并测量可及性以及其他空间和社会经济因素对县级事故严重程度的影响，同时控制时空上未观察到的影响。在其他见解中，结果表明主要道路密度与所有碰撞严重程度呈正相关。相反，相交密度与伤害成负相关，并且没有伤害崩溃。此外，在这种影响下，学校的密度和驾驶率都与无伤害事故成正相关。此外，到医院的平均旅行时间与死亡人数呈正相关，而城市化与致命交通事故呈负相关。由于非结构化随机效应项而产生的未观察到的效应非常重要，并有助于模型的整体拟合优度。碰撞严重程度之间的区域内相关为正且不可忽略。从时间的角度来看，致命和无伤害事故的趋势都在下降。然而，多年来没有观察到伤害事故的清晰模式。从空间角度来看，在整个研究区域中都发现了极高和极低的崩溃风险县，而没有明显的聚集或分散模式。