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. 2021 Jul 4;18(13):7155.
doi: 10.3390/ijerph18137155.

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Susana García-Herrero  1 , Juan Diego Febres  2 , Wafa Boulagouas  1 , José Manuel Gutiérrez  3 , Miguel Ángel Mariscal Saldaña  1
Affiliations

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Susana García-Herrero et al. Int J Environ Res Public Health. .

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.

Keywords: aberrant infractions; bayesian network; road traffic accidents; speed infractions; technology-based distractions; traffic accidents severity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Obtained directed acyclic graph corresponding to the study variables.
See this image and copyright information in PMC

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