Dangerous driving behaviors are a public safety concern. These behaviors are heavily influenced and can be triggered by emotions. Emotions, and their resultant behaviors, are influenced by emotional intelligence (EI), defined as an individual’s ability to identify and differentiate between emotions, and make decisions based on them. The objective of this study was to determine the best EI and dangerous driving behavior measures. This exploratory study examined methods to measure EI and its possible relationship with various measures of dangerous driving behavior among 155 adult drivers in the United States with a valid driver’s license. Participants completed an online survey, which included three scales for EI assessment (TEIQue, GENOS, WLEIS) and three dangerous driving behavior scales (DDDI, DBQ, DBI), along with questions about past crashes and citations. Responses from the survey were analyzed using Pearson’s correlation, linear regression, and binary logistic regression. Significant correlations were observed between all driving behaviors and the TEIQue EI scales. Notably, the TEIQue total EI and emotionality subscales emerged as the most influential predictors of dangerous driving behaviors. Participants with higher scores in total EI and emotionality engaged in fewer dangerous driving behaviors. Furthermore, the GENOS and WLEIS EI scales failed to predict dangerous driving behaviors. This study suggests that driver EI should be analyzed using the TEIQue EI scale and driving behaviors should be measured using the DDDI and DBQ scales. Moreover, promoting and improving EI could be useful in preventing dangerous driving behaviors among drivers and reducing their fatality rates on the road.


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    Title :

    Identifying Measures of Emotional Intelligence and Dangerous Driving


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2023-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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