Unsafe driving behavior and safety law support: unraveling the influence of drivers’ demographics

Understanding how demographic attributes influence risky driving and support for traffic safety laws is essential for developing targeted regulations. This study analyzes self-reported data from Canadian drivers to identify high-risk groups using accident history and acceptance of unsafe behaviors. Two cases are examined: Case 1 focuses on accidents and demerit points; Case 2 focuses on acceptance of risky behaviors. The analysis involves k-means clustering to classify risk groups, factor analysis to group safety regulations into three categories—speeding, distracted/intoxicated driving, and red-light violations—and logistic regression to explore demographic associations. Key findings show that driving experience, income, and region influence risk in Case 1, while vehicle size, driving frequency, gender, age, and income are significant in Case 2. Senior drivers tend to support stricter safety laws opposing distracted and intoxicated driving, over-speeding, and red-light violation. The study’s results can inform targeted driver education, address high-risk groups, and enhance traffic regulation policies.