Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts.

Bastas, Alevizos; Vouros, George A.

doi:10.1016/j.ins.2022.09.015

Cited by 6 publications

(12 citation statements)

References 15 publications

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“…In [126], the authors propose Deep Learning (DL) techniques to model Air Traffic Controllers' (ATCos) reactions in resolving conflicts. The authors focus on the Air Traffic Controllers' (ATCos) reaction prediction problem for Conflict Detection and Resolution (CD&R).…”

Section: Applications Of Autoencoders In Atmmentioning

confidence: 99%

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

et al. 2023

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Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in several domains, and Deep Learning (DL) has attracted attention due to its impressive results and disruptive capabilities. The adoption of DL models in ATM solutions enables new cognitive services that have never been considered before. The main goal of this research is to present a comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM). This review focuses on describing applications, identifying opportunities, and highlighting open challenges to foster the evolution of ATM systems. To accomplish this, we discuss the fundamental topics of DL and ATM and categorize the contributions based on different approaches. First, works are grouped based on the DL approach adopted. Then, future directions are identified based on the ATM solution area. Finally, open challenges are listed for both DL applications and ATM solutions. This article aims to support the community by identifying research problems to be faced in the future.

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Section: Applications Of Autoencoders In Atmmentioning

confidence: 99%

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

et al. 2023

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“…This work formulates the problem of modeling the ATCO policy, specifying how the ATCOs react to resolve conflicts by issuing specific instructions in specific conflicting situations. This, in conjunction with predicting when the ATCOs react to resolve a conflict, as is carried out in [7], paves the way to (a) automating the ATC process, and (b) optimizing the ATC process with respect to ATCOs' preferences and modes of behavior, ensuring enhanced decision making for ATCOs, while leveraging (c) human-AI collaboration in the context of ATC.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, this article reports on data-driven AI/ML methods to model the ATCOs' behavior. Modeling the ATCOs' behavior, as also proposed in [7], implies learning when the ATCOs react to resolve a detected conflict, and how they react. This work formulates the problem of modeling the ATCO policy, specifying how the ATCOs react to resolve conflicts by issuing specific instructions in specific conflicting situations.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts

Bastas

Vouros

2023

Aerospace

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With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model how conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations.

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“…[3,–5]). However, there is a high level of epistemic uncertainty inherent in this approach due to: the simplifications necessarily made by the model; an uncertain knowledge of the aircraft’s state; lack of knowledge of the pilot’s intentions [6]; and the unknown influence of environmental effects on the aircraft trajectory [7]. As a consequence, a mismatch between the predictions of physics-based TP methods and the actual path followed by aircraft can be observed, especially during climbs and descents [8,9].…”

Section: Introductionmentioning

confidence: 99%

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

et al. 2023

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Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant epistemic uncertainty, leading to discrepancies between observed trajectories and the predictions of deterministic models, hampering the task of planning to ensure safe separation. In this paper, a probabilistic model is presented, for the purpose of emulating the trajectories of aircraft in climb and bounding the uncertainty of the predicted trajectory. A monotonic, functional representation exploits the spatio-temporal correlations in the radar observations. Through the use of Gaussian process emulators, features that parameterize the climb are mapped directly to functional outputs, providing a fast approximation, while ensuring that the resulting trajectory is monotonic. The model was applied as a probabilistic digital twin for aircraft in climb and baselined against the base of aircraft data, a deterministic model widely used in industry. When applied to an unseen test dataset, the probabilistic model was found to provide a mean prediction that was 20.56% more accurate, as measured by the mean absolute error, with data-driven credible intervals that were9.54% sharper.

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Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts.

Cited by 6 publications

References 15 publications

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

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