Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3279
Publisher DOI: 10.3390/aerospace8020028
Title: Toward ATM resiliency : a deep CNN to predict number of delayed flights and ATFM delay
Language: English
Authors: Sanaei, Rasoul 
Pinto, Brian Alphonse 
Gollnick, Volker 
Keywords: ATFM delay;CNN;resilience;capacity regulations
Issue Date: 25-Jan-2021
Publisher: Multidisciplinary Digital Publishing Institute
Source: Aerospace 8 (2): 28 (2021)
Journal: Aerospace 
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/8714
DOI: 10.15480/882.3279
ISSN: 2226-4310
Other Identifiers: doi: 10.3390/aerospace8020028
Institute: Lufttransportsysteme M-28 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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