@inproceedings{078dd66552af4c5aad71070e71faca56,
title = "A Deep Learning Approach To Predict General Aviation Traffic Counts",
abstract = "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{\textquoteright}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.",
author = "Amir Abecassis and Daniel Delahaye and Moshe Idan",
note = "Publisher Copyright: {\textcopyright} 2024 by Amir Abecassis, Daniel Delahaye, Moshe Idan.; AIAA SciTech Forum and Exposition, 2024 ; Conference date: 08-01-2024 Through 12-01-2024",
year = "2024",
doi = "https://doi.org/10.2514/6.2024-2701",
language = "الإنجليزيّة",
isbn = "9781624107115",
series = "AIAA SciTech Forum and Exposition, 2024",
booktitle = "AIAA SciTech Forum and Exposition, 2024",
}