Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.

背景：尾随是一种常见的攻击性驾驶行为，已被确定为追尾事故的主要原因之一。以前的研究已经探索了尾随司机的行为，并报告了减少尾随的数量或流行的有效解决方案。本文试图通过专注于理解高速公路尾随场景并使用现有的自然驾驶数据检查领先车辆的反应来填补研究空白​​。方法：通过使用一秒车头时距阈值标准，共识别出 1,255 个尾随事件。识别出领先车辆的四种反应类型，包括变道、减速、加速和无反应。本研究采用随机森林算法，根据驾驶员、车辆和环境变量等相应因素预测领先车辆的反应。结果：对尾随情景和相关因素的分析表明，男性司机比女司机更频繁地参与尾随事件，并且在晴天和白天条件下尾随事件更为普遍。变道是尾随过程中领头车辆最普遍的反应，占事件总数的一半以上。随机森林的结果表明，平均车头时距、尾随持续时间和最小车头时距是三个主要因素，对领先车辆驾驶员的反应影响最大。研究发现，在 95% 的事件中，领先的车辆在被尾随两分钟或更长时间后会变道。实际应用：这项研究的结果有助于更好地了解司机的行为和决策。这种理解可用于设计对策或辅助系统，以减少尾随行为和相关的负面安全后果。