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Toward ATM resiliency: A Deep CNN to predict number of delayed flights and ATFM delay

Sanaei, Rasoul and Pinto, Brian Alphonse and Gollnick, Volker (2021) Toward ATM resiliency: A Deep CNN to predict number of delayed flights and ATFM delay. Aerospace. Multidisciplinary Digital Publishing Institute (MDPI). ISSN 2226-4310.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/140598/
Document Type:Article
Title:Toward ATM resiliency: A Deep CNN to predict number of delayed flights and ATFM delay
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sanaei, RasoulRasoul.Sanaei (at) dlr.dehttps://orcid.org/0000-0001-7063-5114
Pinto, Brian AlphonseTUHH - Hamburg University of TechUNSPECIFIED
Gollnick, VolkerVolker.Gollnick (at) dlr.dehttps://orcid.org/0000-0001-7214-0828
Date:2021
Journal or Publication Title:Aerospace
Refereed publication:No
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2226-4310
Status:Accepted
Keywords:ATFM delay; CNN; Resilience; Capacity Regulations
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Air Traffic Concepts and Operation (old)
Location: Hamburg
Institutes and Institutions:Air Transport Operations > Air Transport Infrastructures & Processes
Deposited By: Sanaei, Rasoul
Deposited On:22 Jan 2021 11:10
Last Modified:22 Jan 2021 11:10

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