Application of Optimization Algorithms in Reliable Pathfinding Models for Smart Cities

This article implements and compares two potential algorithms in urban traffic management: the RAO-3 optimization algorithm and the Q-Learning reinforcement learning algorithm. The research is conducted on the dynamic traffic density optimization system platform, which collects real-time traffic data from IoT devices and processes it through fog computing infrastructure. The implementation of RAO-3 and Q-Learning on this rich dataset can be considered a groundbreaking contribution, helping to identify a more optimal algorithm for traffic flow and routing based on current conditions. The core idea of the research is to manually create a set of sample data while also extracting data from the dynamic traffic density optimization system, then testing this dataset with the RAO-3 and Q-Learning algorithms. The results indicate that Q-Learning outperforms RAO-3 in terms of efficiency and accuracy. This serves as a foundation for future advancements in smart city technology, emphasizing the role of integrating advanced technology in promoting more sustainable, efficient, and safer urban environments.