An analysis of the relationship between driver characteristics and driving safety using structural equation models. (April 2019) - Zhao, Xiaohua
Highlights: In our study, driver characteristics are analyzed with a focus on illegal driving behaviors, and a new method is developed to reduce driver error. However, driver characteristics are complex, and the associated interrelationships among variables are not easily identified. A structural equation model (SEM) is adopted in this paper to capture the complex relationships among related variables. According to the SEM results, the relationship between driver characteristics and driving safety is analyzed. The model results suggest that driver training lessons must be implemented, and traffic issues must be urgently solved. In addition, our results suggest that to improve driving safety, extra training sessions should be required for young females and drivers with bad attitudes and serious road rage. Abstract: Traffic crashes pose a serious threat to global health and have negative impacts on social and economic development. Driving safety could be significantly improved if drivers obtained effective training based on practical suggestions from studies of the relationship between driver characteristics and driving safety. In this study, driver characteristics are analyzed with a focus on illegal driving behaviors, and a new method is developed to reduce driver error. However, driver characteristics are complex, and the associated interrelationships among variables are not easily identified. A structural equation model (SEM) is adopted in this paper to capture the complex relationships among related variables. This model can simultaneously address the complex relationships among endogenous and exogenous variables. We investigate five illegal driving behaviors, including illegal passing, illegal lane changes, speeding, running red lights and distracted driving, based on a driving simulator. Distracted driving is chosen as an example to construct the SEM. We group 13 variables into four latent variables in the model. In this paper, 44 participants (average age ± S.D. = 33 ± 12.8 years, range: 19–55 years; average driving experience ± S.D. = 10 ± 8.8 years, range: 2–30 years) were recruited for the experiment, driving behavior data were collected via a driving simulator and driving attitude data were collected using a questionnaire. Our findings reveal that the main factor that influences illegal driving actions is driving attitude and the main factor that influences distracted driver performance is basic driver characteristics. Moreover, the driver training level is the most significant factor that negatively affects the basic driver characteristics (factor load = −0.91), and anger with slow driving is the main factor that influences the driver attitude (factor load = 0.90). According to the SEM results, the relationship between driver characteristics and driving safety is analyzed. The model results suggest that driver training lessons must be implemented and traffic issues must be urgently solved. In addition, our results suggest that extra training sessions should be required of young females and drivers with bad attitudes and serious road rage to improve driving safety. Although the results are based on distracted driving, the method presented in this paper is suitable for other analyses of driving behavior.