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.
|Number of pages||10|
|Journal||International Journal of Simulation and Process Modelling|
|Publication status||Published - 2019|