Abstract
Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.
Highlights
The road transportation system presents a high-risk system that threatens people’s lives every day [1]
The World Health Organization estimates that approximately 1.35 million people die in road traffic accidents each year, and 20 to 50 million more people suffer non-fatal injuries, which often lead to long-term disabilities [3]
In the USA, the National Highway Safety Administration estimated that distracted driving is responsible for approximately 10% of all fatal traffic accidents [11]
Summary
The road transportation system presents a high-risk system that threatens people’s lives every day [1]. Many researchers have moved toward correlating the analysis methods with prediction techniques to model interactions between risk factors and predict potential impacts on causalities, frequencies, and the severity of traffic accidents Such a combination considers several parameters to analyze the current conditions that are, assessed using the mean of the prediction models that contribute to mitigate the magnitude of traffic accidents and enhance the transportation system and safety strategies [26]. Analysis [29], and Bayesian Networks [30] Building on these attempts, this paper focuses on analyzing the influence of technology-based distracted driving on drivers’ infractions and assesses their subsequent impact on the severity of traffic accidents employing recent data on road traffic accidents in Spain. The remainder of this paper is arranged as follows: Section 2 reviews distracted driving; Section 3 sums up the data and methodology of the study; Section 4 provides the results of the study; Section 5 discusses the results and puts forward main findings, limitations, and future research guideline, while Section 6 concludes the paper
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