Wu Qiong, Zhang Guohui, Zhu Xiaoyu, Liu Xiaoyue Cathy, Tarefder Rafiqul
Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Honolulu, HI 96822, United States.
Metropia, Inc., 1790 E.River Rd., Suite 140, Tucson, AZ 85718, United States.
Accid Anal Prev. 2016 Sep;94:35-45. doi: 10.1016/j.aap.2016.03.026. Epub 2016 May 28.
This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.
本研究利用2010年至2011年在新墨西哥州收集的数据,分析了农村和城市地区单车碰撞事故中驾驶员的受伤严重程度。为了考虑严重程度类别(无受伤、可能受伤、可见受伤、致残受伤和死亡)之间的相关性以及驾驶员之间的个体异质性,开发了嵌套逻辑模型和混合逻辑模型。本研究考察了各种因素,如碰撞和环境特征、几何特征以及驾驶员行为。嵌套逻辑模型和混合逻辑模型在识别驾驶员受伤严重程度的影响因素方面显示出相似的结果。在城市碰撞事故分析中,由于混合逻辑模型中未发现随机参数,因此仅呈现了嵌套逻辑模型。结果表明,农村和城市地区单车碰撞事故中导致驾驶员受伤严重程度的因素存在显著差异。在农村模型中仅发现5个变量具有显著性,在城市碰撞模型中仅识别出6个显著变量。这些发现有助于交通部门制定有效的政策或适当的策略,以降低单车碰撞事故导致的受伤严重程度。