RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic
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 regulations when capacity cannot be adjusted. This proce...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/2/93 |
| _version_ | 1797483895239737344 |
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| author | Sergi Mas-Pujol Esther Salamí Enric Pastor |
| author_facet | Sergi Mas-Pujol Esther Salamí Enric Pastor |
| author_sort | Sergi Mas-Pujol |
| collection | DOAJ |
| description | 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 regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictions. |
| first_indexed | 2024-03-09T22:54:37Z |
| format | Article |
| id | doaj.art-d78146beb980474c9f21876d80ee0444 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T22:54:37Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-d78146beb980474c9f21876d80ee04442023-11-23T18:14:33ZengMDPI AGAerospace2226-43102022-02-01929310.3390/aerospace9020093RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route TrafficSergi Mas-Pujol0Esther Salamí1Enric Pastor2Department of Computer Architecture, Escola d’Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC), Universitat Politècnica de Catalunya (UPC), Esteve Terradas 7, Castelldefels, 08860 Barcelona, SpainDepartment of Computer Architecture, Escola d’Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC), Universitat Politècnica de Catalunya (UPC), Esteve Terradas 7, Castelldefels, 08860 Barcelona, SpainDepartment of Computer Architecture, Escola d’Enginyeria de Telecomunicació i Aeroespacial de Castelldefels (EETAC), Universitat Politècnica de Catalunya (UPC), Esteve Terradas 7, Castelldefels, 08860 Barcelona, SpainMeeting 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 regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictions.https://www.mdpi.com/2226-4310/9/2/93ATFM regulationsdemand–capacity balancingdeep learningconvolutional neural networkrecurrent neural networkRNN-CNN hybrid model |
| spellingShingle | Sergi Mas-Pujol Esther Salamí Enric Pastor RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic Aerospace ATFM regulations demand–capacity balancing deep learning convolutional neural network recurrent neural network RNN-CNN hybrid model |
| title | RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic |
| title_full | RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic |
| title_fullStr | RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic |
| title_full_unstemmed | RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic |
| title_short | RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic |
| title_sort | rnn cnn hybrid model to predict c atc capacity regulations for en route traffic |
| topic | ATFM regulations demand–capacity balancing deep learning convolutional neural network recurrent neural network RNN-CNN hybrid model |
| url | https://www.mdpi.com/2226-4310/9/2/93 |
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