ANALYSING THE ROLE OF DRIVER BEHAVIORS IN ROAD TRAFFIC ACCIDENTS: AN APPLICATION OF MACHINE LEARNING
Keywords:Artificial neutral networks, driving behaviour, drowsy driving, road accidents, errors, mistakes, slips, violations
Continuous innovations are taking place worldwide to develop solutions for problems encountered by human beings. The prevention
of a variety of accidents due to burning, drowning, terrorism, electric shocks, and road traffic is among the important concerns of
researchers and solution developers. Specifically, this current study aims to analyse the contributions of different driver behaviours that resulted in road accidents, followed by proposing a viable solution and reducing the road accident frequencies to benefit society at large. This study employed two methods to analyse data. One was through SEM, and the second was through Artificial Neural Network (ANN). The study is descriptive in nature and it used the survey method to collect sample data from 345 drivers from various professional backgrounds. The questionnaire consisted of independent variables, namely slips, errors, mistakes, lapse violations and unintentional violations. To measure the contributions of these variables towards accidents, age was taken as the moderator. The statistical techniques used included reliability, correlation, and normality analyses, in addition to artificial neural networks and regression analyses. Each factor was found to be a significant contributor to road accidents. Moreover, no significant difference was found in drivers’ behaviour between males and females, but age was found to have a moderating effect on the relationship between driver behaviours and accidents. Additionally, the rate of accidents decreases with the increases in age and vice versa.
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