This paper investigates truck-involved crashes to determine the statistically significant factors that contribute to injury severity under different weather conditions. The analysis uses crash data from the state of Ohio between 2011 and 2015 available from the Highway Safety Information System. To determine if weather conditions should be considered separately for truck safety analyses, parameter transferability tests are conducted; the results suggest that weather conditions should be modeled separately with a high level of statistical confidence. To this end, three separate mixed logit models are estimated for three different weather conditions: normal, rain and snow. The estimated models identify a variety of statistically significant factors influencing the injury severity. Different weather conditions are found to have different contributing effects on injury severity in truck-involved crashes. Rural, rear-end and sideswipe crash parameters were found to have significantly different levels of impact on injury severity. Based on the findings of this study, several countermeasures are suggested: 1) safety and enforcement programs should focus on female truck drivers, 2) a variable speed limit sign should be used to lower speeds of trucks during rainy condition, and 3) trucks should be restricted or prohibited on non-interstates during rainy and snowy conditions. These countermeasures could reduce the number and severity of truck-involved crashes under different weather conditions.

翻译：本文研究了不同天气条件下导致卡车事故伤害严重性的统计显著因素。分析使用了俄亥俄州2011年至2015年间高速公路安全信息系统提供的交通事故数据。为确定在卡车安全分析中是否应单独考虑天气条件，进行了参数可转移性检验；结果表明，应在高统计置信水平下分别对天气条件进行建模。基于此，针对三种不同天气条件（正常、雨雪）分别估计了三个混合Logit模型。模型识别出多种影响伤害严重性的统计显著因素。研究发现，不同天气条件对卡车事故伤害严重性具有不同的影响效应。乡村道路、追尾及侧擦碰撞参数对伤害严重性的影响程度存在显著差异。根据本研究结果，提出以下对策建议：1）安全与执法计划应重点关注女性卡车驾驶员；2）应在降雨条件下使用可变限速标志降低卡车速度；3）在雨雪天气应限制或禁止卡车在非州际公路上行驶。这些对策可有效减少不同天气条件下卡车事故数量及伤害严重程度。