To account for the spatial correlation of crashes that are in close proximity, this study proposes a Bayesian spatial generalized ordered probit (SGOP) model with Leroux conditional autoregressive (CAR) prior for crash severity analysis. Proposed model can accommodate the ordinal nature of injury severity and relax the assumption of monotonic effects of explanatory factors. Additionally, strength of spatial correlation is considered. Results indicate that the proposed SGOP model with Leroux CAR prior outperforms the conventional ordered probit model and SGOP model with intrinsic CAR. There is moderate spatial correlation for the crashes. Results indicate that factors including vehicle type, horizontal curvature, vertical grade, precipitation, visibility, traffic composition, day of the week, crash type, and response time of emergency medical service all affect the crash injury severity. Findings of this study can indicate the effective engineering countermeasures that can mitigate the risk of more severe crashes on the freeways.

为了考虑近距离碰撞的空间相关性，本研究提出了一种贝叶斯空间广义有序概率 (SGOP) 模型，该模型具有 Leroux 条件自回归 (CAR) 先验，用于碰撞严重程度分析。所提出的模型可以适应损伤严重程度的顺序性质，并放宽解释因素单调效应的假设。此外，还考虑了空间相关性的强度。结果表明，提出的具有 Leroux CAR 先验的 SGOP 模型优于传统的有序概率模型和具有内在 CAR 的 SGOP 模型。碰撞具有中等的空间相关性。结果表明，影响因素包括车辆类型、水平曲率、垂直坡度、降水量、能见度、交通构成、星期几、碰撞类型、紧急医疗服务的响应时间和响应时间都会影响碰撞伤害的严重程度。这项研究的结果可以表明有效的工程对策，可以减轻高速公路上更严重的碰撞风险。