Özgür Karaduman
Traffic safety is a multidimensional field shaped not only by infrastructure and vehicles but also by driver behavior and its technological mediation within Intelligent Transportation Systems (ITS). Among these behaviors, aggressive driving and road rage are critical phenomena that directly affect safety and traffic flow. This review examines psychological and societal determinants such as anger, stress, impatience, and cultural norms; Artificial Intelligence (AI) and Machine Learning (ML)-based detection and management approaches using CAN-bus, biometric, and video data; and Advanced Driver Assistance Systems (ADAS) and ITS applications. The study explains how behavioral evidence supports AI-based risk assessment, early-warning mechanisms, and ITS applications by enhancing situational awareness and adaptive response. It also discusses the growing role of data-driven analytics and sensor fusion in predicting and mitigating risky driving patterns in conventional and connected vehicle environments. Recent research shows that aligning human factors insights with model design enhances reliability and supports adaptive, real-time safety functions. The review provides practical implications for researchers and policymakers regarding intelligent behavior modeling, ethical data use, and human-centric ITS design, and highlights future research areas such as cross-cultural analyses, biometric-aware modeling, and human–autonomy interaction in next-generation mobility systems.