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

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Randomized trial explores dynamic traffic management efficiency using deep reinforcement learning, suggesting significant improvements.

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.

Asst. Prof. Patil Ravindra Nimba (2026) studied this question.

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