A Deep Learning Approach To Predict General Aviation Traffic Counts

Amir Abecassis, Daniel Delahaye, Moshe Idan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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’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.

Original languageEnglish
Title of host publicationAIAA SciTech Forum and Exposition, 2024
DOIs
StatePublished - 2024
EventAIAA SciTech Forum and Exposition, 2024 - Orlando, United States
Duration: 8 Jan 202412 Jan 2024

Publication series

NameAIAA SciTech Forum and Exposition, 2024

Conference

ConferenceAIAA SciTech Forum and Exposition, 2024
Country/TerritoryUnited States
CityOrlando
Period8/01/2412/01/24

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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