What question did this study set out to answer?

This research aims to evaluate deep reinforcement learning approaches for managing traffic congestion dynamically.

June 13, 2026Open Access

Dynamic Traffic Management Through Deep Reinforcement Learning

Puntos clave

This research aims to evaluate deep reinforcement learning approaches for managing traffic congestion dynamically.
Developed integrated deep reinforcement learning models for traffic management.
Designed state and action spaces along with a reward function for real-time decision making.
Compared the effectiveness of DRL against traditional traffic management methods.
Demonstrated significant reduction in travel time with DRL compared to traditional methods.
Improved throughput was achieved, indicating enhanced traffic flow efficiency.

Resumen

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.

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