Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai, 201804, China.
Industrial and Manufacturing Systems Engineering Department, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA; University of Michigan Transportation Research Institute, 2901 Baxter Rd, Ann Arbor, MI, 48109-2150, USA.
Accid Anal Prev. 2021 Dec;163:106450. doi: 10.1016/j.aap.2021.106450. Epub 2021 Oct 19.
Collision warning systems can improve traffic safety, while their safety benefit may be lessened due to improper risk compensation or system misuse. There are limited studies of advanced safety systems increasing unexpected risky driving behavior, especially with adolescent drivers. This study is designed to address this research gap in two main areas: 1) it seeks to examine whether and how the introduction of advanced driver-assistance systems influences drivers' risk compensation behavior (e.g., increase of hard braking frequency), and 2) it investigates key factors (e.g., distraction) that contribute to changes in hard braking frequency during driving for both teen and adult drivers. Naturalistic driving data from two previous studies were analyzed in this study with two methods: a hierarchical logistic regression model was used to evaluate the effects of an integrated collision warning system on hard braking behavior, while a Random forests algorithm was applied to model hard braking behavior and to rank the contributing factors by calculating the importance scores. No statistical evidence was observed that the integrated collision warning system significantly changed the likelihood of hard braking for teen or adult drivers. Other factors like distraction, especially visual-manual distraction, had the largest impact on the hard braking behavior, followed by speeding and roadway segments (i.e., at intersections or not). Short time-headways and driving in high-density traffic significantly increased the likelihood of hard braking. Furthermore, the rate of hard braking behavior on surface roads was much higher than on highways, as expected. Compared with straight road segments, hard braking behavior was less likely to occur on curve roads. This study applied an analytical strategy by using both machine learning and statistical analysis methods to achieve high model accuracy and facilitate inference concerning the relationships among variables. Findings in this study can help to improve the design of integrated collision warning systems and the use of autonomous braking systems, and to apply appropriate analysis methods in understanding teen drivers' behavior changes with those safety systems.
碰撞警告系统可以提高交通安全，但其安全效益可能因风险补偿不当或系统误用而降低。目前关于先进安全系统增加意外危险驾驶行为的研究较少，尤其是在青少年驾驶员中。本研究旨在解决两个主要领域的研究空白：1）研究先进驾驶员辅助系统的引入是否以及如何影响驾驶员的风险补偿行为（例如，急刹车频率的增加）；2）研究导致青少年和成年驾驶员驾驶时急刹车频率变化的关键因素（例如，分心）。本研究使用两种方法分析了两项先前研究中的自然驾驶数据：分层逻辑回归模型用于评估集成碰撞警告系统对急刹车行为的影响，随机森林算法用于对急刹车行为进行建模，并通过计算重要性得分对导致急刹车的因素进行排名。没有观察到集成碰撞警告系统显著改变青少年或成年驾驶员急刹车的可能性的统计证据。其他因素，如分心，尤其是视觉-手动分心，对急刹车行为的影响最大，其次是超速和道路路段（即交叉口或非交叉口）。短时间间隔和在高密度交通中行驶显著增加了急刹车的可能性。此外，正如预期的那样，表面道路上的急刹车行为率远高于高速公路。与直道段相比，弯道上急刹车的可能性较小。本研究应用了一种分析策略，同时使用机器学习和统计分析方法，以实现高模型准确性，并有助于推断变量之间的关系。本研究的结果有助于改进集成碰撞警告系统的设计和自动驾驶系统的使用，并应用适当的分析方法来理解青少年驾驶员在使用这些安全系统时的行为变化。
