Prediction of air traffic complexity through a dynamic complexity indicator and machine learning models-Reference-Cited by-同舟云学术

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Prediction of air traffic complexity through a dynamic complexity indicator and machine learning models

Published:2024-08 Issue: Volume:119 Page:102632
ISSN:0969-6997
Container-title:Journal of Air Transport Management
language:en
Short-container-title:Journal of Air Transport Management

Author:

Pérez Moreno Francisco^ORCID,Ibáñez Rodríguez Fernando,Gómez Comendador Víctor Fernando,Delgado-Aguilera Jurado Raquel^ORCID,Zamarreño Suárez María^ORCID,Arnaldo Valdés Rosa María^ORCID

Publisher

Elsevier BV

Reference39 articles.

1. Exploratory analysis of air travel demand stimulation in first-time served markets;Abdelghany;J. Air Transport. Manag.,2021

2. Determining air traffic complexity challenges and future development;Antulov-Fantulin;Promet - Traffic & Transp.,2020

3. Air traffic complexity for a distributed air traffic management system;Brázdilová;Proc. Inst. Mech. Eng. G J. Aerosp. Eng.,2011

4. A knowledge-transfer-based learning framework for airspace operation complexity evaluation;Cao;Transport. Res. C Emerg. Technol.,2018

5. A Survey on artificial intelligence (AI) and eXplainable AI in air traffic management: current trends and development with future research trajectory;Degas;Appl. Sci.,2022

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