Traffic Control

Traffic control research aims to optimize road network efficiency, reduce congestion, and minimize environmental impact, primarily focusing on urban and highway settings. Current efforts leverage machine learning, particularly reinforcement learning and its variants like Proximal Policy Optimization, along with novel approaches such as Differentiable Predictive Control and Large Language Models, to develop adaptive and efficient control strategies for signalized and unsignalized intersections, ramp metering, and coordinated perimeter control. These advancements offer significant potential for improving traffic flow, reducing fuel consumption and emissions, and enhancing overall transportation system performance.