Identifying and modelling correlation between airport weather conditions and additional time in airport arrival sequencing and metering area

Margarita Bagamanova*, Juan José Ramos González, Miquel Àngel Piera Eroles, Jose Manuel Cordero García, Álvaro Rodríguez-Sanz

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Different uncertainties during operational activities of modern airports can significantly delay some processes and cause chain-effect performance drop on the overall air traffic management (ATM) system. The decision-making process to mitigate the propagation of perturbations through the different airport processes can be improved with the support of a causal model, built with a use of data mining and machine learning techniques. This paper introduces a new approach for modelling causal relationships between various ATM performance indicators, which can be used to predict, by means of simulation techniques, the evolution of airport operations scenarios. The analysis of reachable airport states is a relevant approach to design mitigation mechanisms on those perturbations which drive the system to poor KPIs.

Original languageEnglish
Pages (from-to)213-222
Number of pages10
JournalInternational Journal of Simulation and Process Modelling
Volume14
Issue number3
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Airport
  • ASMA time
  • Bayesian networks
  • Coloured Petri net
  • CPN
  • Decision support tool
  • Holding
  • Inbound traffic
  • TMA model
  • Weather impact

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