Smart traffic management system by q learning and using machine learning

The rapid advancement in traffic management systems has paved the way for smarter, more efficient transportation networks. This project introduces a Smart Traffic Management System that combines Q-learning and machine learning algorithms to optimize traffic flow and reduce congestion in real-time. The system incorporates three key algorithms: Random Forest, Q-Learning, and Deep Q-Network (DQN), each contributing uniquely to the management of traffic signals and vehicle flow. The Random Forest model achieves the highest accuracy of 99.21%, providing robust performance in predicting traffic conditions based on various features like weather, congestion, and traffic volume. In contrast, the Q-Learning algorithm, while effective for decision-making in dynamic environments, has a lower performance with an accuracy of 74.99%, indicating room for further optimization. The DQN, a deep reinforcement learning model, strikes a balance with an accuracy of 82.95%, showcasing its ability to learn optimal traffic signal policies in a complex environment. The system uses real-time data to adjust traffic signals, aiming to reduce congestion, enhance safety, and improve overall traffic efficiency. This research demonstrates the potential of integrating traditional machine learning with reinforcement learning in smart city solutions, emphasizing the evolving role of AI in transforming urban traffic systems.

Keywords: Smart Traffic Management, Q-Learning, Deep Q-Network (DQN), Random Forest, Machine Learning, Traffic Optimization, Reinforcement Learning, Real-Time Data, Smart Cities, Urban Mobility, AI Traffic Systems, Congestion Management.