Federated Drift-Aware Graph Neural Forecasting for Real-Time Passenger Flow

Transit operators need accurate and privacy-preserving passenger-flow forecasts to enable dynamic headway control and crowd management. We introduce FedST-GNN, a federated spatio-temporal graph neural network that fuses encrypted federated averaging (FedAvg) with a frequency-domain Transformer and an Adaptive Windowing (ADWIN) - triggered meta-learning loop for fast concept-drift recovery. Experiments on the public Copenhagen-Flow dataset (18.7 M events, 312 stops, 2022–2024) show that FedST-GNN cuts mean-absolute-error by 5% and root-mean-square-error by 7% relative to the strongest deep baseline (Temporal Fusion Transformer), while sustaining a median inference latency of 38 ms on a GTX 1660 SUPER. During a city half-marathon, the ADWIN trigger and two inner meta-updates lowered peak error by 41% without exceeding a 5 MB communication budget per 15-minute federated round. These results demonstrate that privacy-compliant, drift-resilient graph learning can deliver real-time accuracy on commodity hardware, offering a practical blueprint for intelligent transport analytics.