Lightweight Graph Embedding Augmentation for Airport Traffic Forecasting

Short-term airport traffic forecasting faces a structural gap: temporal ensemble models ignore route-network dependencies that shape hub operations, while deep graph neural networks require synchronised multi-airport operational data streams unavailable to single-airport operators, who have access only to their own operational records and publicly available route topology. To our knowledge, this study provides the first systematic evaluation of three graph representation classes—centrality measures, DeepWalk, and Node2Vec—as structural augmentations to Random Forest (RF), XGBoost, and LightGBM for hourly aircraft movement prediction at King Khalid International Airport (RUH), using a two-hop aviation graph combining RUH operational data with the OpenFlights database. Across all three ensemble families, random-walk graph augmentations consistently reduce MAE by approximately 9–17% relative to temporal-only baselines, whereas handcrafted centrality measures provide smaller and less consistent gains. Diebold–Mariano tests confirm that both RF+DeepWalk and RF+Node2Vec significantly outperform all nine baseline models (p