Analysis of operational data to identify emerging safety risks
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hdl:2117/359292
Correu electrònic de l'autorHERNANDEZPINTOABELGMAIL.COM
Tipus de documentTreball Final de Grau
Data2021-12-22
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Abstract
In view of the paramount importance of safety in the Air Traffic Management system, EUROCONTROL has been developing risk models for ATM to quantify and characterize how safe the different airspaces inside the ECAC region are. The risk models mainly characterize accidents by means of the quantification of barriers that avoid precursors becoming accidents due to their good functioning. While barriers are defined as probabilities of failure, and are quantified thanks to Fault Tree Analysis, where every element is a contributor to failure, precursors are quantified as frequencies of occurrence, and are based upon experience. This provides a static view of the models, and does not take into consideration more dynamic aspects such as the traffic flow characteristics, sector complexity, and many other features not used in the risk models because they would not allow generalization, and could only refer to specific airspaces as small as simple sectors. In view of the previous statements, this project is born with the objective to find a machine learning model capable of predicting air traffic control interventions for potential conflict avoidance inside an en-route sector from the FIR Península in Spain. The idea is to build a model explainable enough to provide sufficient information about complexity and traffic flow features in an en-route sector inducing tactical conflicts (precursors in the EUROCONTROL en-route Mid-Air Collision risk model), and offer a complementary vision of the given model. To develop the model, an analysis of operational data from the Spanish ANSP ENAIRE is made, accompanied by a study to find the losses of separation and potential tactical conflicts (mid-air collision accident precursors) during a 29-day period with a conflict detector tool. The last step is the training and testing of two tree-based supervised learning models. The outcome of the supervised learning model allows the identification of the flow and complexity features that induce air traffic control interventions to avoid potential tactical conflicts and LoS with a 99.79% of certainty in conflict prediction in a single en-route sector. Future evolutions of the model will consider the tactical ATCO support, conflict resolution with reinforcement learning, and finally the automation of ATCO tasks.
TitulacióGRAU EN ENGINYERIA DE SISTEMES AEROESPACIALS (Pla 2015)
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memoria.pdf | 5,561Mb | Accés restringit |