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Analyzing the Influences of Driver Distractions Based on Driver's Subjective Cognition

机译:基于驾驶员主观认知的驾驶员分心影响分析

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摘要

The study is intended to investigate the prevalence and severity of 13 types of driver distractions. Two types of survey, an anonymous online questionnaire and a field distribution and recycling questionnaire, have been performed to collect the data. Four hundred and four respondents contributed to the survey. Independent sample T-test and one-way ANOVA method are used to analyze the influences of driver personality traits (i.e., gender, professional, age, and driving experience) on the prevalence of driver distractions. In addition, clustering analysis method is used to the classification of driver distractions. Results indicated that the most frequent distraction was "Listening to music," and the most dangerous distraction was "Writing text messages." Three personality traits (professional, age, and driving experience) were found to have a significant impact on the prevalence of driver distractions. According to the influence factors, driver distractions could be divided into three categories: low, middle, and high. The multiple resource theory was used to explain the rationality of categories in the view of cognition. The results could contribute to the drivers' subjective perceptions of each driver distraction and offer the basis for the further research on driver distraction and road safety.
机译:该研究旨在调查13种类型的驾驶员分心的发生率和严重性。进行了两种类型的调查,即匿名在线调查表和现场分布和回收调查表,以收集数据。 404位受访者为调查做出了贡献。使用独立样本T检验和单向方差分析法来分析驾驶员的性格特征(即性别,职业,年龄和驾驶经验)对驾驶员分心的发生率的影响。另外,采用聚类分析方法对驾驶员分心进行分类。结果表明,最频繁的干扰是“听音乐”,而最危险的干扰是“写短信”。发现三个人格特征(专业,年龄和驾驶经验)对驾驶员分心的发生率有重要影响。根据影响因素,驾驶员分心可以分为三类:低,中和高。运用多元资源理论从认知的角度解释范畴的合理性。该结果可有助于驾驶员对每种驾驶员分心的主观感知,并为进一步研究驾驶员分心和道路安全提供基础。

著录项

  • 来源
  • 会议地点 Nanjing(CN)
  • 作者单位

    Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;

    Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;

    Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;

    Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;

    Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Driver distractions; Personality traits; Distraction classification; Multiple resource theory;

    机译:驾驶员分心;人格特质;干扰分类;多元资源理论;

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