Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
This research developed a Proximal Policy Optimization (PPO) reinforcement learning controller for adaptive traffic signals in Kuwait using IoT sensor data. The PPO agent dynamically adjusts green light durations based on real-time traffic states, significantly reducing vehicle delay and emissions compared to fixed-time and actuated controls. The system proved robust against demand changes and generalized well to different traffic patterns.