Patterns of distracted driving behaviors among young adult drivers: Exploring relationships with personality variables

- Most common distractions: talk with passengers, program music, eat/ drink, navigate.
- Latent profile analysis revealed 3 profile classes of distracted driving.
- The profile with lowest distracted behavior rated behaviors as most distracting.
- One profile rated behaviors as moderately distracting, despite highest engagement.
- Extraversion relates to more distracted driving, despite moderate risk perceptions.

The current study sought to identify naturally occurring profiles of distracted driving behaviors; to distinguish personality traits associated with these profiles; and lastly to examine relationships between distracted driving behaviors and perception of risk. Self-reported information on distracted driving behaviors (i.e., frequency of engagement and perceived level of distraction) and personality characteristics were collected online from 266 young adult drivers from two academic institutions. The most commonly reported distracting behaviors were talking with passengers, programming music, eating or drinking, and using navigation. Latent profile analysis revealed 3 profile classes of distracted driving, with the personality trait of extraversion emerging as a consistent predictive indicator of level of distracted driving, including in high-risk situations such as inclement weather and in free-flowing, high speed traffic. In terms of level of perceived risk, the profile class with the lowest engagement in distracted driving behavior tended to rate the behaviors at most distracting, relative to higher-engagement groups. The profile class that engaged in the highest level of distracted driving reported such behaviors as moderately distracting, despite engaging in them. This class also yielded the highest levels of extraversion, suggesting that this personality trait may relate to consistently higher levels of distracted driving behaviors, despite perceiving them as risky. This information may be useful for identifying young adults at highest risk of engaging in distracted driving, as well as for tailoring initiatives to decrease rates of distracted driving, potentially cutting down incidents in this high-risk population.