Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies

Abstract

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.