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Application of Optimization Algorithms in Reliable Pathfinding Models for Smart Cities

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This research compares RAO-3 and Q-Learning algorithms in traffic management, highlighting data from IoT devices.

Q-Learning outperformed RAO-3 in efficiency and accuracy for urban traffic management.
The analysis used real-time traffic data collected from IoT devices integrated with fog computing.
Comparative testing of algorithms on dynamic traffic density optimization systems provides insights for urban planning.
Findings suggest significant improvements in traffic flow management through advanced optimization algorithms.

Le et al. (2025) studied this question.

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