TY - JOUR
T1 - Uncertainty Management at the Airport Transit View
AU - Rodríguez-Sanz, Álvaro
AU - Comendador, Fernando Gómez
AU - Valdés, Rosa Arnaldo
AU - García, Jose Manuel Cordero
AU - Bagamanova, Margarita
N1 - Funding Information:
We would like to thank the editors and the anonymous reviewers for their careful reading of our manuscript and their insightful comments and suggestions. We believe that the quality of the manuscript has been significantly improved as a result.
Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
KW - Airport operations
KW - Bayesian networks
KW - Business Process Modelling
KW - Delay propagation
KW - System congestion
UR - http://www.scopus.com/inward/record.url?scp=85065483095&partnerID=8YFLogxK
U2 - 10.3390/aerospace5020059
DO - 10.3390/aerospace5020059
M3 - Article
AN - SCOPUS:85065483095
VL - 5
JO - Aerospace
JF - Aerospace
SN - 2226-4310
IS - 2
M1 - 59
ER -