Abstract:
Road traffic accidents claim millions of lives annually and impose
significant economic and societal costs. The relationship between key
contributing factors and road traffic violations has been underexplored, while
previous studies primarily focused on accident prediction. This study
examined the impact of traffic violations across the top eight urban and rural
states in the United States, analyzing the main causing factors, which were
assessed using the Random Forest Machine Learning Model. Performance
metrics, including accuracy, F-measure, precision, and Kappa statistics,
validated the model’s effectiveness. Partial dependence plots explored the
relationships between violations and contributing factors. In rural settings,
violations were driven mainly by speeding, negligent driving, overcorrecting,
and non-owner drivers. In urban areas, reckless driving, drug use, improper
lane usage, tailgating, and failure to yield were the predominant factors. These
findings underscore the need for tailored interventions to address area-specific
violations, helping policymakers implement strategies to reduce violations
and improve road safety.
