Transformer-Driven Dynamic Forecasting and Scheduling Optimization of Tourist Flow
TL;DR
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.
Abstract
Tourist flow modeling and prediction in scenic areas is highly complex, exhibiting significant spatiotemporal dependence and being influenced by various external factors. Traditional models struggle to simultaneously characterize complex spatiotemporal relationships and integrate diverse external information, posing challenges for intelligent resource management and dynamic information scheduling in large-scale networked systems. To address this, this paper proposes 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. The framework integrates heterogeneous data such as historical traffic, weather, and social media, achieving adaptive time-varying spatial embedding through a graph attention network (GAT) while capturing long- and short-term dependencies using a Temporal Fusion Transformer (TFT) for both point and quantile prediction. The PSO algorithm performs global optimization of the TFT-GAT hyperparameters, and the resulting prediction outputs together with uncertainty estimates are incorporated into the D3QN to realize closed-loop online capacity allocation based on reinforcement learning. Such a data-driven spatiotemporal modeling strategy also provides methodological insights for dynamic information fusion and adaptive resource scheduling in intelligent electromagnetic sensing and communication environments. Experiments conducted at the Forbidden City in Beijing demonstrate high prediction accuracy with a mean absolute error of 1.5–2.6 people/hour, an average quantile coverage exceeding 82%, and a response time of 140.6 ms. The incorporation of exogenous factors, particularly holidays, improves prediction performance by 18.4%, validating the proposed framework’s robustness in spatiotemporal coupling, uncertainty representation, and real-time scheduling.