Crash severity of a hazardous material (HAZMAT) transporting truck increases manyfold compared with normal truck crash because of the possible exposure to dangerous substances. Crashes which involve a HAZMAT truck might result in a catastrophic incident causing horrendous damage to individuals involved in the crash. In-transit HAZMAT crashes in Wyoming caused a total damage of $3.1 million from 2015 to 2018. HAZMAT crashes on interstate roads represented 22% of the total HAZMAT crashes causing a total damage of $2.2 million, representing 71% of the cost of total damage. Previous studies in Wyoming investigated all vehicle crashes, including large truck crashes, but none has analyzed HAZMAT-related crashes or accounted for its type as a contributing factor. This study fills the gap by analyzing crash injury severity of HAZMAT-related crashes on all interstate freeways in Wyoming. Furthermore, the study introduces the No-U-Turn (NUT) Hamiltonian Monte Carlo (HMC) method of hierarchical Bayesian analysis into HAZMAT crash injury severity analysis. In recent developments, NUT HMC has been proven to be the most efficient Markov Chain Monte Carlo (MCMC) sampling method. The results showed that 30% of the unobserved heterogeneity arises from variation in summer and winter crashes which justifies the use of hierarchical model. Among the other covariates investigated, the population-averaged effects showed that number of trucks involved, hit-and-run crashes, animal-vehicle crashes, work-zone-related crashes, collision type, percentage of females involved, drivers’ drug/alcohol use, seat-belt use, crash location, roadway curves, and surface conditions significantly impact HAZMAT crash injury severity.

危险材料（HAZMAT）运输卡车的碰撞严重性与正常卡车碰撞相比增加了很多倍，因为可能会暴露于危险物质中。涉及HAZMAT卡车的撞车事故可能会导致灾难性事件，对撞车事故涉及的个人造成可怕的伤害。从2015年到2018年，怀俄明州在途HAZMAT撞车造成的总损失为310万美元。州际公路上的HAZMAT撞车占HAZMAT撞车总数的22％，造成的总损失为220万美元，占总损失的71％。怀俄明州的先前研究调查了所有车辆碰撞，包括大型卡车碰撞，但没有人分析与HAZMAT相关的碰撞或将其类型归因于此。本研究通过分析怀俄明州所有州际高速公路上与HAZMAT相关的撞车的撞车伤严重性来填补这一空白。此外，该研究将分层贝叶斯分析的No-U-Turn（NUT）哈密顿蒙特卡洛（HMC）方法引入HAZMAT碰撞伤害严重性分析。在最近的发展中，NUT HMC已被证明是最有效的马尔可夫链蒙特卡洛（MCMC）采样方法。结果表明，30％的未观察到的异质性来自夏季和冬季车祸的变化，这证明了使用分层模型的合理性。在调查的其他协变量中，总体平均影响表明，涉及的卡车数量，撞车事故，动物车辆撞车，与工作区有关的撞车，撞车类型，所涉女性比例，驾驶员的毒品/酒精含量使用，安全带使用，