Distracted Driving Crashes: A Review on Data Collection, Analysis, and Crash Prevention Methods

Abstract

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.

Evaluating Distraction Safety Performance Indicators in an Urban Area of a Low- or Middle-Income Country: A Case Study of Yaoundé, Cameroon

“…Surveys, naturalistic in-vehicle observation and field observation are the three main types of data collection that can be used to assess the prevalence of distracted driving [36,[54][55][56]. Surveys consist of a driver self-reporting their distracted driving attitude, they can be advantageous for gathering data for a specific type of secondary task [57], or from drivers of a specified age range [58,59], but there are generally no means of verifying the information reported by drivers, making the prevalence of self-report distracted driving difficult to rely on for policy design.…”

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network

“…Precise identification of the different types of distractions can contribute to the implementation of more oriented prevention measures and strategies. Therefore, gaining a better understanding of driver’s state during moments of distraction and further recognizing distraction types could contribute to reducing the risk of traffic accidents [ 7 , 8 ] and providing the reliable knowledge for supporting advanced intelligent driving systems [ 9 ].…”

A Driving Simulator Study to Understand the Impact of Cell Phone Blocking Apps on Distraction

“…Distracted driving is one of the main causes of traffic fatalities around the world [1]. It also accounts for 40% of all crashes in the United States (U.S.).…”

“…Many studies have investigated the impacts of cell phone use on drivers' crash risk [1,[20][21][22], and many studies used driving simulators to investigate driving behaviors [23][24][25][26][27][28].…”