Gender and driving safety in Saudi Arabia: Challenging stereotypes with simulator evidence

Introduction: Females were granted the right to drive on public roads in Saudi Arabia (SA) in 2018. The country is undergoing significant policy transformation to promote inclusion, equity, and diversity. However, cultural perceptions and traditional misconceptions on female driving performance are still not fully addressed by scientific research. Method: This driving simulator study involved a total of 100 participants, comprising 60 males and 40 females from Dammam, SA. It examines the influence of gender on crash frequency, violation patterns, and reaction times, while also assessing the impact of weather conditions. Results: The findings indicate that male drivers exhibited slightly higher average crash frequency and loss of control incidents compared to female drivers, although females committed more stop-sign violations. Males also engaged in more aggressive maneuvers, such as sudden lane changes (0.70 vs. 0.28). Despite a longer average reaction time among female drivers (2.04 s vs. 1.80s, p = 0.032), gender was not a significant predictor of crash likelihood (p = 0.337). Instead, weather conditions emerged as a key factor, with rainy conditions significantly increasing crash rates and loss of control incidents. Conclusion: While reaction time was associated with crash frequency, the study found no statistical evidence that gender independently influenced crash risk. These findings challenge traditional stereotypes regarding female driving performance in SA. Findings also show that female drivers are equally, if not more, capable than their male counterparts in many aspects of driving behavior. Practical application: The research findings can serve as a reference to policymakers, traffic safety authorities, and licensing bodies toward promoting a gender-neutral approach in driver licensing and training programs. The results also support public awareness efforts aimed at reducing societal stigma by shifting the narrative from gender-based assumptions to evidence-based assessments of driving ability.