4.6 Article

Dangerous Driving in a Chinese Sample: Associations with Morningness-Eveningness Preference and Personality

期刊

PLOS ONE
卷 10, 期 1, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0116717

关键词

-

资金

  1. National Natural Science Foundation of China [31100750, 31400886, 91124003]
  2. Basic Project of National Science and Technology of China [2009FY110100]

向作者/读者索取更多资源

Individual differences in morningness-eveningness preference may influence susceptibility and response to sleepiness. These differences could influence driving performance, but the influence of morningness-eveningness preference on driving behavior and accident risk has not been comprehensively studied. As morningness-eveningness preference is associated with personality characteristics, we also investigated how the interaction between morningness-eveningness preference and personality may be related to dangerous driving behaviors. Two hundred and ninety five drivers completed the reduced Morningness-Eveningness Questionnaire, the Dula Dangerous Driving Index, and personality scales for agreeableness, conscientiousness and neuroticism, and reported demographic information (gender, age, level of education, driving years and annual average driving mileage) and self-reported traffic violations (accidents, penalty points and fines). The results showed that more Risky Driving, Aggressive Driving, Negative Cognitive/Emotional Driving and Drunk Driving, as measured by the Dula Dangerous Driving Index, were all significantly correlated with more eveningness, corresponding to lower scores on the reduced Morningness-Eveningness Questionnaire. Moreover, eveningness was correlated with self-reported traffic accidents, penalty points and fines. Furthermore, a moderation effect was found: eveningness was more strongly associated with risky driving and negative emotional driving in those who scored high for trait agreeableness.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据