<b>Research on Quantum Approximate Optimization Algorithm Enhanced Low-Altitude Traffic Monitoring for Urban Congestion Prediction and Dynamic Path Optimization</b>

Urban traffic congestion has become increasingly severe, and traditional prediction and optimization methods struggle to meet the demands of real-time dynamic decision-making. This paper proposes a hybrid computational framework that integrates the Quantum Approximate Optimization Algorithm with a spatiotemporal graph attention network, achieving end-to-end joint solving of urban congestion prediction and dynamic path optimization. In the congestion prediction module, a multi-scale spatiotemporal graph attention encoder is designed, which models differentiated influence weights between nodes through a spatial graph attention mechanism, captures dependency relationships across three temporal scales—recent moments, daily periodicity, and weekly periodicity—using multi-scale causal convolution, and introduces adaptive graph structure learning to discover implicit spatial correlations.