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  4. Toward ATM resiliency : a deep CNN to predict number of delayed flights and ATFM delay
 
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Toward ATM resiliency : a deep CNN to predict number of delayed flights and ATFM delay

Citation Link: https://doi.org/10.15480/882.3279
Publikationstyp
Journal Article
Date Issued
2021-01-25
Sprache
English
Author(s)
Sanaei, Rasoul 
Pinto, Brian Alphonse  
Gollnick, Volker  
Institut
Lufttransportsysteme M-28  
TORE-DOI
10.15480/882.3279
TORE-URI
http://hdl.handle.net/11420/8714
Journal
Aerospace  
Volume
8
Issue
2
Article Number
28
Citation
Aerospace 8 (2): 28 (2021)
Publisher DOI
10.3390/aerospace8020028
Scopus ID
2-s2.0-85099970922
Publisher
Multidisciplinary Digital Publishing Institute
The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.
Subjects
ATFM delay
CNN
resilience
capacity regulations
DDC Class
380: Handel, Kommunikation, Verkehr
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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