September 25, 2025Frontiers in Robotics and AIOpen Access

Real-time traffic signal optimization for urban mobility: a reinforcement learning-enhanced framework with application to Kuwait City

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Algorithmic approach improves traffic flow and reduces congestion in smart cities, indicating potential environmental benefits.

The proposed system achieves a speedup of over 7× compared to general-purpose processing units, enhancing real-time traffic management.
Reinforcement Learning algorithms effectively minimize wait times, reduce congestion, and improve urban mobility under various traffic conditions.
Microscopic traffic simulations validate the proposed method's performance against traditional traffic control strategies.
This traffic optimization solution addresses Kuwait’s traffic challenges, potentially improving air quality and reducing emissions.

Almomany et al. (2025) studied this question.

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September 25, 2025Frontiers in Robotics and AIOpen Access

View Full Paper

Ask AI

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Authors

Algorithmic approach improves traffic flow and reduces congestion in smart cities, indicating potential environmental benefits.

The proposed system achieves a speedup of over 7× compared to general-purpose processing units, enhancing real-time traffic management.
Reinforcement Learning algorithms effectively minimize wait times, reduce congestion, and improve urban mobility under various traffic conditions.
Microscopic traffic simulations validate the proposed method's performance against traditional traffic control strategies.
This traffic optimization solution addresses Kuwait’s traffic challenges, potentially improving air quality and reducing emissions.

Almomany et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark