Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction. (3rd April 2018)

• Record Type: Journal Article
• Authors: Papantoniou, Panagiotis
• Is Part Of: Traffic injury prevention. Volume 19:Number 3(2018)
• Page Start: 317
• Page End: 325
• Publication Date: 2018-04-03
• Subjects: Driving performance -- structural equation models -- driving simulator -- road safety -- driver distraction; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2017.1398825
• Languages: English
• ISSNs: 1538-9588

Abstract

ABSTRACT: Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures. Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures. Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance. Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.