Traffic congestion is a pervasive and significant urban challenge, leading to economic losses, increased emissions, and reduced quality of life. Traditional traffic management methods such as fixed-time signaling or reactive approaches are often inadequate for dynamically evolving conditions. Deep Reinforcement Learning (DRL) offers a powerful framework for addressing this, combining deep learning's capabilities for perception with reinforcement learning's decision-making abilities for complex, real-time scenarios. This paper explores integrated DRL approaches for dynamic traffic management, detailing system modeling, state and action space design, and reward function formulation. Conceptual results demonstrate superior performance in reducing travel time and improving throughput compared to traditional methods.
Asst. Prof. Patil Ravindra Nimba (Fri,) studied this question.