Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24060, United States.
Virginia Polytechnic Institute and State University, 750 Drillfield Drive, 200 Patton Hall, Blacksburg, VA 24061, United States.
J Safety Res. 2020 Jun;73:199-209. doi: 10.1016/j.jsr.2020.03.005. Epub 2020 Apr 2.
Crashes involving roadway objects and animals can cause severe injuries and property damages and are a major concern for the traveling public, state transportation agencies, and the automotive industry. This project involved an in-depth investigation of such crashes based on the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data including detailed information and videos about 2,689 events.
The research team conducted a variety of logistic regression analyses, complemented by Support Vector Machine (SVM) analyses and detailed case studies.
The logistic regression results indicated that driver behavior/errors, involvement of secondary tasks, roadway characteristics, lighting condition, and pavement surface condition are among the factors that contributed significantly to the occurrence and/or increased severity outcomes of crashes involving roadway objects and animals. Among these factors, improper turning movements (odds ratio = 88), avoiding animal or other vehicle (odds ratio = 38), and reaching/moving object in vehicle (odds ratio = 29) particularly increased the odds of crash occurrence. Factors such as open country roadways, sign/signal violation, unfamiliar with roadway, fatigue/drowsiness, and speeding significantly increased the severity outcomes when such crashes occurred. The sensitivity analysis of the three SVM classifiers confirmed that driver behavior/errors, critical speed, struck object type, and reaction time were major factors affecting the occurrence and severity outcomes of events involving roadway objects and animals. Practical Applications: The study provides insights on risk factors influencing safety events involving roadway objects, including their occurrence and the severity outcomes. The findings allow researchers and traffic engineers to better understand the causes of such crashes and therefore develop more effective roadway- and vehicle- based countermeasures.
涉及道路物体和动物的碰撞事故可能导致严重伤害和财产损失，这是广大出行者、州交通机构和汽车行业关注的主要问题。本项目基于第二战略公路研究计划（SHRP2）自然驾驶研究（NDS）数据，对这些碰撞事故进行了深入调查，其中包括有关 2689 起事件的详细信息和视频。
研究团队进行了多种逻辑回归分析，并辅以支持向量机（SVM）分析和详细案例研究。
逻辑回归结果表明，驾驶员行为/错误、次要任务的参与、道路特征、照明条件和路面状况是导致涉及道路物体和动物的碰撞事故发生和/或严重程度增加的重要因素。在这些因素中，不当转弯动作（比值比=88）、避免动物或其他车辆（比值比=38）以及在车内触及/移动物体（比值比=29）特别增加了碰撞事故发生的几率。在发生此类碰撞事故时，诸如乡村道路、标志/信号违规、不熟悉道路、疲劳/困倦和超速等因素显著增加了严重程度的后果。三个 SVM 分类器的敏感性分析证实，驾驶员行为/错误、临界速度、撞击物体类型和反应时间是影响涉及道路物体和动物的事件发生和严重程度结果的主要因素。
该研究提供了有关影响涉及道路物体的安全事件的风险因素的见解，包括这些事件的发生和严重程度的后果。研究结果使研究人员和交通工程师能够更好地了解此类碰撞事故的原因，从而制定更有效的基于道路和车辆的对策。
