Traffic Signal

Traffic signal control aims to optimize traffic flow and safety at intersections, a crucial aspect of urban transportation management. Current research focuses on developing adaptive control strategies, employing machine learning techniques like reinforcement learning and gradient boosting to predict optimal signal timing based on real-time data (e.g., vehicle probe data) or to react to unexpected events such as signal malfunctions. These advancements leverage innovative representations of traffic conditions, such as "efficient pressure," to improve the efficiency and robustness of control algorithms, ultimately aiming to reduce congestion and improve overall transportation efficiency.