SmartFlow with DecongesQN: Dynamic Traffic Optimization Using Deep Q-Network Reinforcement Learning

SmartFlow revolutionizes urban traffic management through advanced technologies and adaptive strategies. Central to SmartFlow is DecongesQN, a Deep Q-Network (DQN) based reinforcement learning model that dynamically adjusts traffic signals to optimise flow and enhance efficiency in real-time. Utilising convolutional neural networks, DecongesQN accurately detects and tracks vehicles, enabling precise congestion assessment. This paper presents SmartFlow’s architecture, the integration of DecongesQN, simulation results, and real-world implementations, demon- strating its scalability and effectiveness. Beyond adaptive signal control, SmartFlow contributes to safer and more sustainable urban transportation networks. With its innovative approach, SmartFlow with DecongesQN emerges as a promising solution to address the challenges of modern urban mobility, paving the way for smarter and more resilient cities.