Assessing the relationship between self-reported driving behaviors and driver risk using a naturalistic driving study
Assessing the relationship between self-reported driving behaviors and driver risk using a naturalistic driving study
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使用自然驾驶研究评估自我报告的驾驶行为与驾驶员风险之间的关系
DOI：
10.1016/j.aap.2019.03.009
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发表时间：
2019-07-01
影响因子：
5.9
通讯作者：
Xu, Xiaoyan
中科院分区：
文献类型：
--
作者：
Wang, Xuesong;Xu, Xiaoyan
曼彻斯特司机行为问卷(DBQ)确定了由心理机制导致的危险驾驶行为。调查这些行为与驾驶员碰撞风险之间的关系可以更好地了解导致碰撞发生的个人因素，从而更有效地开展安全教育和道路管理对策和干预。因此，本研究的目的是：1)确定驾驶员在碰撞和近距离碰撞(CNCS)中的参与程度与自我报告的驾驶行为之间的关系；2)评估每种类型的危险行为与个体驾驶员的NC风险之间的关系。驾驶员和撞车数据来自上海自然主义驾驶研究，参与者包括45名男性和12名女性，平均年龄为38.7岁。采用K均值聚类方法将参与者分为高风险、中风险和低风险司机三组。司机完成了DBQ，以自我评估他们每天驾驶调查问卷中24种危险行为的频率。通过对24个条目的主成分分析，得出了攻击性违规、普通违规、失误、疏忽、注意力不集中、经验不足等五个维度的结构。建立了两个Logistic回归模型来研究DBQ的五个组成部分与驾驶员的计算机控制水平之间的相关性。结论如下：1)高风险司机比其他司机更有可能犯下注意力不集中的错误(例如，错过一个减速标志)和普通违章行为(例如，闯红灯)；以及，2)激进违章行为(例如，与他人赛车)和普通违章行为与成为高风险或中等风险司机的概率呈正相关。
The Manchester Driver Behavior Questionnaire (DBQ) identifies risky driving behaviors resulting from psychological mechanisms. Investigating the relationships between these behaviors and drivers' crash risk can provide a better understanding of the personal factors contributing to the incidence of crashes, allowing the more effective development of safety education and road management countermeasures and interventions. The objectives of this study are therefore: 1) to determine the extent to which driver involvement in both crashes and near crashes (CNCs) is related to self-reported driving behaviors, and 2) to assess the relationship between each type of risky behavior and individual driver CNC risk. Driver and crash data were acquired from the Shanghai Naturalistic Driving Study and included 45 males and 12 females, participants with the mean age of 38.7. A K-mean cluster method was adopted to classify participants into three CNC groups of high-, moderate- and low-risk drivers. Drivers completed the DBQ to self-evaluate the frequency during their daily driving of the questionnaire's 24 risky behaviors. Principal component analysis of the 24 items led to a five-component structure including aggressive violations, ordinary violations, lapses, inattention errors, and inexperience errors. Two logistic regression models were developed to investigate the correlation between the five DBQ components and drivers' CNC levels. Conclusions are as follows: 1) high-risk drivers were significantly more likely to have engaged in inattention errors (e.g., missing a "yield" sign) and ordinary violations (e.g., running a red light) than the other drivers, and, 2) aggressive violations (e.g., racing against others) and ordinary violations were positively related to the probability of being a high- or moderate-risk driver.