Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning

ArXi:2605.10170v1 Announce Type: new Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adapt to dynamic conditions, leading to inefficiencies. This paper proposes a novel deep reinforcement learning agent for traffic light control that addresses this limitation by explicitly integrating fairness considerations for both vehicular and pedestrian traffic.