Airport Delay Prediction Based on Spatiotemporal Graph Convolutional Networks

Airport delay prediction is a key technological support for proactive intervention in the air transportation system, enhancing decision-making for airlines, air traffic control authorities, and airport operators. Accurate forecasts optimize resource allocation and alleviate delay impacts. However, the complex spatiotemporal nature of airport networks and delay propagation poses significant challenges, as traditional statistical, machine learning, and single-series forecasting approaches often fail to capture intricate dependencies. To address this, we propose a graph convolutional network (GCN)–transformer–gated recurrent unit (GRU) model. First, an adjacency matrix derived from historical delays encodes spatial relationships, which are extracted by the GCN. Then, a transformer encoder models global temporal dependencies via self-attention. Finally, the GRU integrates multi-source inputs such as weather data for multi-step decoding to predict delays across future intervals. Case studies on China’s 2023 airport operation dataset show that our model outperforms random forest, long short-term memory, standalone GRU, spatiotemporal GCN, and attention-based spatiotemporal GCN models, yielding lower prediction errors and better model fit. Incorporating delay-based adjacency and weather factors further enhances performance. These results demonstrate a clear advantage in prediction accuracy and decision support.