Resilience and new technology : Automation Program II part II: report D11
Woltjer, Rogier LU
; Stefansson, Boel
and Bjursten Carlsson, Christian
(2024)
- Abstract
- This report describes a study that applies a modified methodology for the assessment of resilience of Air Traffic Management with higher degrees of automation in, with particular focus on the assessment of Artificial Intelligence (AI)-based Air Traffic Management tools. Resilience can be described as the ability of the ATM functional system to adjust its functioning and performance goals, prior to, during, or following varying conditions, or in short, to its adaptive capacity. A modified version of the SESAR Resilience Engineering method developed earlier was applied to an early prototype demonstration of an AI-enabled Flight Information Service application. The methodology was modified by performing a combination of questionnaires to... (More)
- This report describes a study that applies a modified methodology for the assessment of resilience of Air Traffic Management with higher degrees of automation in, with particular focus on the assessment of Artificial Intelligence (AI)-based Air Traffic Management tools. Resilience can be described as the ability of the ATM functional system to adjust its functioning and performance goals, prior to, during, or following varying conditions, or in short, to its adaptive capacity. A modified version of the SESAR Resilience Engineering method developed earlier was applied to an early prototype demonstration of an AI-enabled Flight Information Service application. The methodology was modified by performing a combination of questionnaires to pilots (flight instructors) and air traffic controllers and a workshop employing focus group methodology with experienced air traffic controllers with flying experience. The main questions in both methods focused on what characterises good Flight Information Service, as it works today, and as performed by an envisioned AI tool. Data analysis through thematic coding resulted in eight themes: 1. Adapting to context 2. Strategies of work 3. Understanding of user needs 4. Uncertainty management 5. Communication/coordination 6. Preconditions of work 7. Cognitive aspects of work 8. Information aspects FIS is recognized by informants to be highly dependent on adapting to new circumstances, being flexible, improvising, and adapting to the situation where necessary. Air traffic controllers providing FIS generally emphasize the need for adapting their communication to context. Sudden changes in, for example, airspace allocation, weather conditions, and traffic patterns and situations are mentioned as characteristics of good FIS, implying that the AI-FIS will also need to be able to do so. Air traffic controllers currently have a way of working that is partly formalized and partly improvised and adapt based on experience. These two aspects together comprise the FIS methodology, considering prioritization needs and the controller’s desire to always provide the highest level of service possible, timing their actions appropriately in the context of operations, and being as proactive as possible. Controllers develop a feel for and hone their skills and strategies to provide good service as part of FIS based on long experience and understanding of user needs. FIS goes further than just providing information, it could be seen as a form of decision support at times. Uncertainty management is a central skill that controllers currently perform while performing good service levels of FIS, with incomplete, uncertain, and potentially incorrect information adding to the complexity of the task. These aspects, taken together, set a high bar for AI-based FIS. Also, a number of benefits and potential advantages of AI-FIS are highlighted by the informants. The study indicates that the modified method for Resilience Engineering for resilience assessment may be applied to AI-based future technologies for Air Traffic Management, using a dual methods approach consisting of focus group workshops and questionnaires with open resilience-based questions. The different themes, arguments, and examples that emerged from the data are aimed to be used as inputs to the further development and operational implementation of the AI-FIS system. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ef4deff0-d522-4434-972f-c63013962103
- author
- Woltjer, Rogier
LU
; Stefansson, Boel
and Bjursten Carlsson, Christian
- organization
- publishing date
- 2024-11-29
- type
- Book/Report
- publication status
- published
- subject
- keywords
- Aviation, Air traffic services, AI
- pages
- 50 pages
- publisher
- Luftfartsverket (LVF)
- language
- English
- LU publication?
- yes
- additional info
- URN: urn:nbn:se:trafikverket:diva-20039 Arkivnummer: TRV 2020/13208 OAI: oai:DiVA.org:trafikverket-20039 DiVA, id: diva2:1993256 Projekt: Automationsprogrammet II område D
- id
- ef4deff0-d522-4434-972f-c63013962103
- alternative location
- https://trafikverket.diva-portal.org/smash/record.jsf?pid=diva2%3A1993256&dswid=2079
- date added to LUP
- 2025-09-03 09:17:25
- date last changed
- 2025-09-04 09:29:02
@techreport{ef4deff0-d522-4434-972f-c63013962103,
abstract = {{This report describes a study that applies a modified methodology for the assessment of resilience of Air Traffic Management with higher degrees of automation in, with particular focus on the assessment of Artificial Intelligence (AI)-based Air Traffic Management tools. Resilience can be described as the ability of the ATM functional system to adjust its functioning and performance goals, prior to, during, or following varying conditions, or in short, to its adaptive capacity. A modified version of the SESAR Resilience Engineering method developed earlier was applied to an early prototype demonstration of an AI-enabled Flight Information Service application. The methodology was modified by performing a combination of questionnaires to pilots (flight instructors) and air traffic controllers and a workshop employing focus group methodology with experienced air traffic controllers with flying experience. The main questions in both methods focused on what characterises good Flight Information Service, as it works today, and as performed by an envisioned AI tool. Data analysis through thematic coding resulted in eight themes: 1. Adapting to context 2. Strategies of work 3. Understanding of user needs 4. Uncertainty management 5. Communication/coordination 6. Preconditions of work 7. Cognitive aspects of work 8. Information aspects FIS is recognized by informants to be highly dependent on adapting to new circumstances, being flexible, improvising, and adapting to the situation where necessary. Air traffic controllers providing FIS generally emphasize the need for adapting their communication to context. Sudden changes in, for example, airspace allocation, weather conditions, and traffic patterns and situations are mentioned as characteristics of good FIS, implying that the AI-FIS will also need to be able to do so. Air traffic controllers currently have a way of working that is partly formalized and partly improvised and adapt based on experience. These two aspects together comprise the FIS methodology, considering prioritization needs and the controller’s desire to always provide the highest level of service possible, timing their actions appropriately in the context of operations, and being as proactive as possible. Controllers develop a feel for and hone their skills and strategies to provide good service as part of FIS based on long experience and understanding of user needs. FIS goes further than just providing information, it could be seen as a form of decision support at times. Uncertainty management is a central skill that controllers currently perform while performing good service levels of FIS, with incomplete, uncertain, and potentially incorrect information adding to the complexity of the task. These aspects, taken together, set a high bar for AI-based FIS. Also, a number of benefits and potential advantages of AI-FIS are highlighted by the informants. The study indicates that the modified method for Resilience Engineering for resilience assessment may be applied to AI-based future technologies for Air Traffic Management, using a dual methods approach consisting of focus group workshops and questionnaires with open resilience-based questions. The different themes, arguments, and examples that emerged from the data are aimed to be used as inputs to the further development and operational implementation of the AI-FIS system.}},
author = {{Woltjer, Rogier and Stefansson, Boel and Bjursten Carlsson, Christian}},
institution = {{Luftfartsverket (LVF)}},
keywords = {{Aviation; Air traffic services; AI}},
language = {{eng}},
month = {{11}},
title = {{Resilience and new technology : Automation Program II part II: report D11}},
url = {{https://trafikverket.diva-portal.org/smash/record.jsf?pid=diva2%3A1993256&dswid=2079}},
year = {{2024}},
}