Distracted driving is one of the top three reasons for traffic fatalities. Every year, thousands of people are injured or killed in motor vehicle crashes resulting from distracted driving and recent technological advancements have increased the sources and frequency of distractions. This study provides a comprehensive literature review and a summary of findings for identifying best practices to collect and analyze data on distracted driving and countermeasures to mitigate distracted driving. It identifies literature published since 2006 that focuses exclusively on distracted driving. The results found that the severity of crashes involving distracted driving depends primarily on driver behavior and the geometric design of roadway and temporal variables. It was also found that several techniques exist to collect driver behavior data using dashcam cameras integrated into the dashboard of vehicles. For the detection of distracted driving, deep learning techniques are most often used by researchers. It is also found that the integration of the three Es approach in countermeasures is needed to mitigate distracted driving. These findings will help decision-makers comprehend the significant contributing factors associated with crashes involving distracted driving and implement the necessary data collection, data analysis, and practical treatments to reduce the crash severity. Based on the literature review findings, future research recommendations to address distracted driving are proposed.
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