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Cited by 168Air Traffic Management and OptimizationExplainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

A survey on artificial intelligence (ai) and explainable ai in air traffic management: current trends and development with future research trajectory

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Degas, Augustin, Islam, Mir Riyanul, Hurter, Christophe, Barua, Shaibal, Rahman, Hamidur, Poudel, Minesh, Ruscio, Daniele, Ahmed, Mobyen Uddin +8 more·2022·Applied Sciences
DOI 10.3390/app12031295
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Abstract

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

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Authors
Degas, A., Islam, M. R., Hurter, C., Barua, S., Rahman, H., Poudel, M., Ruscio, D., Ahmed, M. U., Begum, S., Rahman, M. A., Bonelli, S., Cartocci, G., Di Flumeri, G., Borghini, G., Babiloni, F., & Aricó, P.
Affiliations
  • École Nationale de l’Aviation Civile (FR)
  • Mälardalen University (SE)
  • Deep Blue (Italy) (IT)
  • Sapienza University of Rome (IT)
  • Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
Corresponding author
  • Degas, Augustin
  • Islam, Mir Riyanul
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-01-26
Volume
12
Issue
3
Pages
1295
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app12031295
OpenAlex
W4210263542
Cited by
168
References
14
Citations by year
2026: 45 · 2025: 47 · 2024: 34 · 2023: 33 · 2022: 9
FWCI
13.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • HAL (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
  • PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
Funders
European Union’s Horizon 2020 within the framework SESAR 2020 research and innovation program (894238)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
air traffic management, explainable AI, artificial intelligence in aviation, aviation safety

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