Characterization and prediction of the airport operational saturation

This paper develops a functional analysis of the aircraft flow through the airport operational framework, focusing on the airspace-airside integrated system. In this analysis, the authors use a dynamic spatial boundary associated with the Extended Terminal Maneuvering Area (E-TMA) concept, so inbound and outbound timestamps can be considered. Aircraft operations are characterized by several temporal milestones, which arise from the combination of a Business Process Model (BPM) for the aircraft flow and the Airport Collaborative Decision Making (A-CDM) methodology. This timestamp approach allows us to study the successive hierarchical tasks. The objective is to establish a taxonomy that classifies the system's capacity to “receive and transmit” aircraft streams with adherence to the expected schedule. By considering the accumulated delay across the different processes and its evolution, several indicators are proposed to evaluate the system's level of saturation and its ability to ensure an appropriate aircraft flow in terms of time-efficiency. Finally, the relationships between the factors that influence the aircraft flow are evaluated to create a probabilistic graphical model, using a Bayesian Network (BN) approach. This model predicts outbound delays given the probability of having different values at the causal control variables. The methodology is developed and validated through a case study at Adolfo Suárez Madrid-Barajas Airport (LEMD): a collection of nearly 34,000 turnaround operations (registered at the peak traffic months of 2016) is used to statistically determine the aircraft path characteristics. The contribution of the paper is twofold: it presents a novel methodological approach to evaluate and predict the system's state at the rotation stage and it also provides insights on the interdependencies between factors influencing performance.


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  • Accession Number: 01672293
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 2 2018 11:04AM