Publication: Forecasting driving distraction using data mining analysis.
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Date
2022-07
Authors
Mohd Nazrol, Nurul Atikah
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Abstract
Distracted driving poses significant safety risk factors to road users. The source of distractions varies from visual, manual, auditory also cognitive. Research has been devoted to analyzing driving distraction based on secondary tasks, mobile phones, and type of roadway effect. However, insufficient analysis was carried out to forecast driving distraction levels. Few studies have considered the effect of gender and the relationship between driving performance measures. Therefore, this research aims to identify potential metrics that indicate driving distraction state, determine gender effect on driving distraction levels., and create a classification model that forecasts driving distraction levels based on driving performance metrics. The case study datasets that involve eight sessions of experimental driving on a simulator were retrieved from the Open Science Framework public databases. Preprocessing techniques were applied to eliminate outliers, extreme values, and irrelevant instances. The datasets are trained on 10-fold cross-validation classification using all the algorithms to determine three distracted driving levels: low, moderate, and high. The classification performances are compared with the ZeroR baseline algorithms. Outcomes show that the percentage accuracy obtained from the OneR classification model performed best, giving 99.6674% % accuracy. Findings also show that female is prone to distraction compared to male.