Uncertainty Management at the Airport Transit View
Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Aerospace |
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| Online Access: | http://www.mdpi.com/2226-4310/5/2/59 |
| _version_ | 1818902875386937344 |
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| author | Álvaro Rodríguez-Sanz Fernando Gómez Comendador Rosa Arnaldo Valdés Jose Manuel Cordero García Margarita Bagamanova |
| author_facet | Álvaro Rodríguez-Sanz Fernando Gómez Comendador Rosa Arnaldo Valdés Jose Manuel Cordero García Margarita Bagamanova |
| author_sort | Álvaro Rodríguez-Sanz |
| collection | DOAJ |
| description | Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance. |
| first_indexed | 2024-12-19T20:42:36Z |
| format | Article |
| id | doaj.art-9b9ec0f0ef3945b8bb602919f3dd5461 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-12-19T20:42:36Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-9b9ec0f0ef3945b8bb602919f3dd54612022-12-21T20:06:22ZengMDPI AGAerospace2226-43102018-06-01525910.3390/aerospace5020059aerospace5020059Uncertainty Management at the Airport Transit ViewÁlvaro Rodríguez-Sanz0Fernando Gómez Comendador1Rosa Arnaldo Valdés2Jose Manuel Cordero García3Margarita Bagamanova4Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros N3, 28040 Madrid, SpainDepartment of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros N3, 28040 Madrid, SpainDepartment of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros N3, 28040 Madrid, SpainCRIDA A.I.E. (Reference Center for Research, Development and Innovation in ATM), Avenida de Aragón N402 Edificio Allende, 28022 Madrid, SpainDepartment of Telecommunications and Systems Engineering, Universitat Autònoma de Barcelona (UAB), Carrer de Emprius N2, 08202 Sabadell, SpainAir traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance.http://www.mdpi.com/2226-4310/5/2/59airport operationssystem congestiondelay propagationBusiness Process ModellingBayesian networks |
| spellingShingle | Álvaro Rodríguez-Sanz Fernando Gómez Comendador Rosa Arnaldo Valdés Jose Manuel Cordero García Margarita Bagamanova Uncertainty Management at the Airport Transit View Aerospace airport operations system congestion delay propagation Business Process Modelling Bayesian networks |
| title | Uncertainty Management at the Airport Transit View |
| title_full | Uncertainty Management at the Airport Transit View |
| title_fullStr | Uncertainty Management at the Airport Transit View |
| title_full_unstemmed | Uncertainty Management at the Airport Transit View |
| title_short | Uncertainty Management at the Airport Transit View |
| title_sort | uncertainty management at the airport transit view |
| topic | airport operations system congestion delay propagation Business Process Modelling Bayesian networks |
| url | http://www.mdpi.com/2226-4310/5/2/59 |
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