Resilient Capacity Management: Predicting General Aviation Traffic Counts with Deep Learning

An accurate forecast of general aviation traffic is crucial for air navigation service providers, as it affects the overall efficiency of air traffic management and capacity planning. This paper presents a deep learning methodology for predicting general aviation traffic, combining calendar and meteorological sources through a detailed feature-engineering procedure. The approach is rigorously evaluated using historical data from the Nice Cote D’Azur Terminal Control Center sectors, resulting in a significant 32% enhancement in global prediction performance with recurrent neural network models compared to existing operational tools. The paper explores additional analysis techniques to gain a deeper understanding of the predictions that are produced by each model.