A Deep Learning Approach To Predict General Aviation Traffic Counts

Amir Abecassis (1) , Daniel Delahaye (2, 1) , Moshe Idan (3)

1 OPTIM - ENAC - Equipe Optim
2 DGAC - Direction Générale de l'Aviation Civile
3 Technion - Israel Institute of Technology [Haifa]

General Aviation traffic prediction is a major concern for Air Navigation Service Providers with a direct impact on air traffic flow and capacity management measures. This paper introduces a Deep Learning methodology using meteorological and calendar data to predict General Aviation traffic. The methodology is evaluated in great detail using historical data from the Nice Cote D'Azur Terminal Control Center sectors with an increase of the global prediction performance of 32% with Recurrent Neural networks-based models compared to current tools used in operation. Additional tools are finally proposed to analyze and attain an in-depth understanding of the predictions generated by the various models.

Keywords: Machine learning; General aviation; RNN

Domains: Artificial Intelligence [cs.AI]

Amir Abecassis, Daniel Delahaye, Moshe Idan. A Deep Learning Approach To Predict General Aviation Traffic Counts. AIAA SciTech Forum, AIAA, Jan 2024, Orlando (Florida), United States. ⟨10.2514/6.2024-2701⟩. ⟨hal-04413100⟩