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Multiple Linear Regression Model of Defensive Driving Technology and Driver Emotional Intelligence Improvement

Jiangsu Automobile Technician Institute, Yangzhou Jiangsu 225003, China
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Abstract

In recent years, defensive driving technology has become an important research direction for road transport safety risk prevention and control. However, the research on defensive driving at home and abroad focuses on the feasibility, judgment, and recognition of defensive driving, and a quantitative research on the relationship between defensive driving training and driver emotional intelligence remains lacking. A multiple linear regression model to represent the relationship between defensive driving training and driver emotional intelligence improvement is proposed. This model is divided into two stages to describe the linear relationship between the five independent variables of driver psychology, physiology, emotion, driving intelligence, and learning time and the dependent variables of defensive driving training effectiveness. An F-test is carried out to verify the linear characteristics of the model. Results show that the average error probability of the linear impact of the five independent variables on the dependent variables is 0.047 in the two stages, which meets the requirements of multiple linear regression. The results prove that the proposed model is effective. Analysis of the model indicates that defensive driving training has two distinct characteristics on the improvement of driver emotional intelligence. In the initial stage, defensive driving training has the highest correlation with indicators related to driver agility. In the second stage, defensive driving training is highly correlated with indicators related to the psychological and emotional control of drivers.

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Journal of Highway and Transportation Research and Development (English Edition)
Pages 102-110

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Cite this article:
Qi X. Multiple Linear Regression Model of Defensive Driving Technology and Driver Emotional Intelligence Improvement. Journal of Highway and Transportation Research and Development (English Edition), 2020, 14(2): 102-110. https://doi.org/10.1061/JHTRCQ.0000735

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Received: 09 January 2020
Published: 01 June 2020
© The Editorial Office of Journal of Highway and Transportation Research and Development (English Edition)