The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach
By Grigorios Fountas, Sarvani Sonduru Pantangi, Kevin F. Hulme, Panagiotis Ch. Anastasopoulos. Analytic Methods in Accident Research, 2019
167 citations recorded by the source corpus.
Abstract Previous research has shown that the determinants of perceived and observed aggressive driving behavior may differ. However, the consideration of major sources of aggressive patterns may introduce additional variations in the effect of such determinants. This study aims to provide further insights in the variations of these two behavioral components arising from driver’s fatigue, gender as well as internal and external distractions (such as, rushing to destination, listening to music and solving logical problems) during the driving task. To identify how the factors determining perceived and observed aggressive behavior may vary across groups of drivers associated with such sources of aggressive driving, survey and simulation data are statistically analyzed. Separate models of perceived and observed aggressive driving behavior are estimated for fatigued and non-fatigued, distracted and non-distracted, male and female drivers. To address various aspects of unobserved heterogeneity, associated with the unobserved variations that are commonly shared among the behavioral components and participants, as well as their unobserved interactions, the correlated grouped random parameters bivariate probit modeling framework is employed. The results of the empirical analysis showed that the effect of the socio-demographic and behavioral factors on perceived and aggressive driving behavior may vary across the aforementioned groups of drivers, in terms of magnitude and directional effect. In addition, the identification of correlation among the unobserved characteristics further illustrates the complexities of the driving decision mechanism, especially when fundamental sources of aggressive driving are evident.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality, Transportation
View Paper: https://www.semanticscholar.org/paper/49c5bca869c2b16953bccc8aa0ebf3d409bb32df
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In Safety, Risk, Reliability and Quality
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Citations in the corpus
- Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression (2019, background): “…in the unstructured heterogeneous effects is statistically insignificant in all three of the models, perhaps because the correlations among the crash severities are mainly in their spatial effects and spatio-temporal interactive effects (Barua, El-Basyouny, and Islam 2014; Fountas et al. 2019).”