Air Traffic Flow Prediction in Aviation Networks Using a Multi-Dimensional Spatiotemporal Framework

Abstract

A novel, multi-dimensional, spatiotemporal prediction framework is proposed to enhance air traffic flow prediction in increasingly complex aviation networks. This framework incorporates graph convolutional networks (GCNs) with multi-dimensional Long Short-Term Memory (LSTM) networks and multi-scale, temporal convolution, employing an attention mechanism to effectively capture spatiotemporal dependencies. By addressing irregular topologies and dynamic temporal trends, the framework models local air traffic patterns with improved accuracy. The experimental results demonstrate significant predictive accuracy improvements over traditional methods, particularly in accounting for the complex nature of air traffic flows. The model’s scalability and adaptability extend its application to various aviation networks, encompassing all airspace units within three local networks, rather than focusing solely on airport traffic. These findings contribute to the development of more intelligent, accurate, and adaptive air traffic management systems, ultimately enhancing both operational efficiency and safety.