Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies

Predicting airport arrival flow serves as a crucial technique in air traffic flow management. Given the unique operational characteristics of air traffic systems, airport arrival flow simultaneously presents complex dynamics in spatial–temporal dimensions, specific spatial heterogeneity, non-rigid periodicity, and robust plannability. These factors pose challenges to existing modeling methods in achieving optimal performance. To address these challenges, we propose a novel large-range air traffic flow prediction model to forecast airport arrival flow. More specifically, a dynamic multi-graph neural network is designed to automatically capture the time-evolving and heterogeneous spatial correlations using convolution and attention operations. In terms of the temporal dimension, a temporal-aware attention module is constructed to extract the temporal transitions of traffic data, considering both the local context and stationarity of the traffic representation sequence. Furthermore, a prior-guided recalibration fusion module is employed to explicitly incorporate the prior knowledge, including historical-periodic and future-scheduled arrival flow features, to recalibrate the temporal module prediction results, thereby enhancing the prediction accuracy. Experimental results on a real-world airport traffic flow dataset demonstrate that the proposed method outperforms the state-of-the-art baselines, and all proposed technical modules contribute to desired performance improvements. •A full dynamic graph is built based on multiple airport operation concepts.•A DMGNN block is to capture traffic situation from both local and global views.•A TAA module is to mine the local trend and global stationarity of traffic features.•A PRF block is to recalibrate the prediction using priors of air traffic operations.