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Article

13 Citations

4,444 Views

25 Pages

RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic

Sergi Mas-Pujol,
Esther Salamí and
Enric Pastor

Aerospace2022, 9(2), 93;https://doi.org/10.3390/aerospace9020093

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10 February 2022

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management...

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Article

14 Citations

4,223 Views

22 Pages

Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay

Rasoul Sanaei,
Brian Alphonse Pinto and
Volker Gollnick

Aerospace2021, 8(2), 28;https://doi.org/10.3390/aerospace8020028

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25 January 2021

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...

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Article

1,629 Views

21 Pages

Predictability of Flight Arrival Times Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Vladimir Socha,
Miroslav Spak,
Michal Matowicki,
Lenka Hanakova,
Lubos Socha and
Umer Asgher

Aerospace2024, 11(12), 991;https://doi.org/10.3390/aerospace11120991

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30 November 2024

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital...

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