交通运输系统工程与信息

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (5): 114-119.

• 系统工程理论与方法 • 上一篇    下一篇

智能手机使用行为对驾驶可靠性的影响模型

傅志妍1, 2，陈坚*1，陈林3   

  1. 1. 重庆交通大学交通运输学院，重庆 400074；2. 重庆第二师范学院经济与工商管理学院，重庆 400067； 3. 中冶赛迪工程技术股份有限公司，重庆 400013
  • 收稿日期:2019-01-17 修回日期:2019-05-14 出版日期:2019-10-25 发布日期:2019-10-25
  • 作者简介:傅志妍(1984-)，女，山西太原人，讲师，博士生.
  • 基金资助:

    国家社会科学基金西部项目/National Social Science Foundation of China(17XGL009).

Influence Model of Smart Phone Using Behavior on Driving Reliability

FU Zhi-yan1, 2, CHEN Jian1, CHEN Lin3   

  1. 1. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. College of Economics & Business Administration, Chongqing University of Education, Chongqing 400067, China; 3. CISDI Engineering Co., Ltd, Chongqing 400013, China
  • Received:2019-01-17 Revised:2019-05-14 Online:2019-10-25 Published:2019-10-25

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摘要：

为解决智能手机使用行为对驾驶安全影响过程缺少定量描述的问题，基于驾驶行为信息加工和注意分配理论，探索智能手机使用行为与驾驶可靠性的关联关系，提出7 个因果关系假设，构建涵盖视觉资源消耗、认知资源消耗、驾驶不可靠性等潜变量的智能手机使用行为影响的结构方程模型. 通过线上与线下相结合的方式进行驾驶人问卷调查，实证分析结果表明，视觉资源消耗(0.448)、认知资源消耗(0.256)对驾驶可靠性具有直接负向效应，信息输入、显示观看、语音通话及心理特征具有间接负向效应，其中显示观看(0.450)的效应最大.

关键词: 智能交通, 驾驶行为, 注意分配, 结构方程模型, 可靠性

Abstract:

There is the problem of lacking the quantitative description for the process that using smartphone can affect driving safety. Based on the theories of driving behavior information processing and attention distribution, this study aims to explore the relationship between smartphone using behavior and driving reliability. Seven causality hypotheses are proposed in this study to construct the structural equation model describing the process of how smartphone using behavior can affect driving safety. The latent variables explaining the driving safety include visual resource consumption, cognitive resource consumption, and driving unreliability. The dataset is obtained by online surveys and face-to-face questionnaire. The empirical analysis shows: visual resource consumption (0.448) and cognitive resource consumption (0.256) have direct negative effects on driving reliability; information input, display viewing, voice calls, and psychological features have an indirect negative effect on driving reliability of which the factor of viewing (0.450) has the greatest effect.

Key words: intelligent transportation, driving behavior, attention distribution, structural equation model, reliability

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