W.-L. Zhang

Transformer-Driven Dynamic Forecasting and Scheduling Optimization of Tourist Flow

A closed-loop integration framework that combines a particle swarm optimization (PSO)-based Temporal Fusion Transformer-Graph Attention Network (TFT-GAT) prediction model with Deep Double-Q Network (D3QN) scheduling optimization is proposed, validating the proposed framework’s robustness in spatiotemporal coupling, uncertainty representation, and real-time scheduling.