Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions
Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/379 |
| _version_ | 1798034809836011520 |
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| author | Victor Fernando Gomez Comendador Rosa Maria Arnaldo Valdés Manuel Villegas Diaz Eva Puntero Parla Danlin Zheng |
| author_facet | Victor Fernando Gomez Comendador Rosa Maria Arnaldo Valdés Manuel Villegas Diaz Eva Puntero Parla Danlin Zheng |
| author_sort | Victor Fernando Gomez Comendador |
| collection | DOAJ |
| description | Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators„ in the “complexity metrics„. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve. |
| first_indexed | 2024-04-11T20:49:32Z |
| format | Article |
| id | doaj.art-daec0c77f380417dbf89a88c1a06e799 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:49:32Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-daec0c77f380417dbf89a88c1a06e7992022-12-22T04:03:53ZengMDPI AGEntropy1099-43002019-04-0121437910.3390/e21040379e21040379Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance SolutionsVictor Fernando Gomez Comendador0Rosa Maria Arnaldo Valdés1Manuel Villegas Diaz2Eva Puntero Parla3Danlin Zheng4Air Transport and Airports Department, School of Aerospace Engineering, Technical University of Madrid (UPM), 28040 Madrid, SpainAir Transport and Airports Department, School of Aerospace Engineering, Technical University of Madrid (UPM), 28040 Madrid, SpainAir Transport and Airports Department, School of Aerospace Engineering, Technical University of Madrid (UPM), 28040 Madrid, SpainATM Research and Development Reference Centre (CRIDA), 28022 Madrid, SpainATM Research and Development Reference Centre (CRIDA), 28022 Madrid, SpainDemand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators„ in the “complexity metrics„. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.https://www.mdpi.com/1099-4300/21/4/379Bayesian networkscomplexityuncertaintyTBOSESAR Capacity ManagementDCBDACFCA |
| spellingShingle | Victor Fernando Gomez Comendador Rosa Maria Arnaldo Valdés Manuel Villegas Diaz Eva Puntero Parla Danlin Zheng Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions Entropy Bayesian networks complexity uncertainty TBO SESAR Capacity Management DCB DAC FCA |
| title | Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions |
| title_full | Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions |
| title_fullStr | Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions |
| title_full_unstemmed | Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions |
| title_short | Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions |
| title_sort | bayesian network modelling of atc complexity metrics for future sesar demand and capacity balance solutions |
| topic | Bayesian networks complexity uncertainty TBO SESAR Capacity Management DCB DAC FCA |
| url | https://www.mdpi.com/1099-4300/21/4/379 |
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