Graduate Research Assistant Department of Civil and Architectural Engineering University of Wyoming 1000 E. University Ave., Rm 3071 Laramie, WY 82071 United States.
Department of Civil and Architectural Engineering University of Wyoming 1000 E. University Ave., EERB 407B Laramie, WY 82071 United States.
Accid Anal Prev. 2020 Sep;144:105654. doi: 10.1016/j.aap.2020.105654. Epub 2020 Jun 26.
Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007-2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.
早期关于卡车事故伤害严重程度的研究主要集中在单辆卡车和涉及多辆卡车的事故，或者根据农村和城市位置、一天中的时间变化、照明条件、道路分类和天气条件研究涉及卡车的伤害严重程度。然而，不同车辆与卡车碰撞对相应乘客伤害严重程度的影响尚不清楚。因此，本文通过对基于车辆类型（即单辆卡车、卡车-汽车、卡车-SUV/皮卡和卡车-卡车）的卡车事故中乘客伤害严重程度进行广泛评估，推进了当前的研究，并确定了主要的乘客、事故和几何相关的促成因素。进行了一系列对数似然比检验，以证明按车辆和乘客类型分别建模是合理的。使用怀俄明州 I-80 十年的事故数据（2007-2016 年），通过使用贝叶斯推断方法的二元逻辑建模，为每种车型和乘客类型开发了伤害严重程度模型。建模结果表明，当按车辆类型分解并按乘客类型分开时，卡车事故中各种变量对伤害严重程度的影响存在显著差异。乘客的年龄和性别、卡车司机的职业、司机的居住地、侧面碰撞、路口的存在、下坡、弯道和天气条件对不同车辆与卡车碰撞中的乘客伤害严重程度有显著不同的影响。最后，在建模过程中加入随机截距，确定了同一事故和同一车辆内的人员之间伤害严重程度的内碰撞和车内相关性（共同事故和车辆特定未观察到因素的影响）的存在。
