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Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay
Content Provider | MDPI |
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Author | Sanaei, Rasoul Pinto, Brian Alphonse Gollnick, Volker |
Copyright Year | 2021 |
Description | 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. |
Starting Page | 28 |
e-ISSN | 22264310 |
DOI | 10.3390/aerospace8020028 |
Journal | Aerospace |
Issue Number | 2 |
Volume Number | 8 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-01-25 |
Access Restriction | Open |
Subject Keyword | Aerospace Transportation Science and Technology Atfm Delay Cnn Resilience Capacity Regulations |
Content Type | Text |
Resource Type | Article |