Application of Risk-Driven Control in Transportation System

Transportation systems are complex mega systems fraught with uncertainty and risk. Traditional traffic control and management methods, often based on deterministic or expected-value optimization, struggle to effectively address tail risks arising from traffic accidents, congestion propagation, and emergencies. As an emerging paradigm, risk-driven control introduces rigorous risk measurement tools to directly quantify and manage the distributional risks of transportation system performance, enabling more nuanced trade-offs between safety, efficiency, and robustness. This paper systematically reviews the theoretical advancements and practical applications of risk-driven control in transportation. First, it elucidates the necessity and core concepts of risk-driven control within the transportation context. Subsequently, it summarizes research outcomes across key subfields: autonomous vehicles, intelligent connected transportation, traffic network management and control, as well as public and multimodal transportation. Finally, it delves into current research challenges, such as computational complexity and verification difficulties, while outlining future research directions.