PPO Traffic Control Output

This dataset presents SUMO simulation results from a study employing a Proximal Policy Optimization (PPO) reinforcement learning model for dynamic traffic signal control at a single intersection. The simulation, set in a mixed traffic environment, integrates diverse vehicle types and realistic traffic patterns to train an intelligent controller. The PPO model utilizes state information such as vehicle counts, waiting times, and signal phases, optimizing the traffic flow by reducing waiting times, minimizing queue lengths, and increasing throughput. This concise output offers valuable insights for researchers exploring adaptive traffic management and intelligent transportation systems in urban environments with promising performance results.