The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Human Factors Measurement for Future Air Traffic Control Systems
Abstract
Objective: This article provides a critical review of research pertaining to the measurement of human factors (HF) issues in current and future air traffic control (ATC). Background: Growing worldwide air traffic demands call for a radical departure from current ATC systems. Future systems will have a fundamental impact on the roles and responsibilities of ATC officers (ATCOs). Valid and reliable methods of assessing HF issues associated with these changes, such as a potential increase (or decrease) in workload, are of utmost importance for advancing theory and for designing systems, procedures, and training. Method: We outline major aviation changes and how these relate to five key HF issues in ATC. Measures are outlined, compared, and evaluated and are followed by guidelines for assessing these issues in the ATC domain. Recommendations for future research are presented. Results: A review of the literature suggests that situational awareness and workload have been widely researched and assessed using a variety of measures, but researchers have neglected the areas of trust, stress, and boredom. We make recommendations for use of particular measures and the construction of new measures. Conclusion: It is predicted that, given the changing role of ATCOs and profound future airspace requirements and configurations, issues of stress, trust, and boredom will become more significant. Researchers should develop and/or refine existing measures of all five key HF issues to assess their impact on ATCO performance. Furthermore, these issues should be considered in a holistic manner. Application: The current article provides an evaluation of research and measures used in HF research on ATC that will aid research and ATC measurement.
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“For example, Langan-Fox et al. (2009, p. 896) refer to human–automation teams as “the dynamic, interdependent coupling between one or more human operators and one or more automated systems requiring collaboration and coordination to achieve successful task completion”.”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Langan-Fox et al. (2009)
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“Recently, Merritt and Ilgen (2008) expressed the need for increased specificity in the conceptualization and measurement of trust, suggesting that instruments should be developed that assess trust in a multidimensional manner.”
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“Nonetheless, stress has been measured in various ways; levels of ATCO stress have been documented in terms of physiological adaptations, such as changes in hormonal secretion (Maxwell, 1986; Wetherell, Hyland, & Harris, 2004), heart rate (Collet et al., 2009; Kumar, Weippert, Vilbrandt, Kruezfeld, & Stoll, 2007; Ming…”
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200 of 240 shown
Cited by
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Understanding teamwork errors in royal air force air traffic control
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Metadata
- Authors
- Langan-Fox, J., Sankey, M. J., & Canty, J. M.
- Affiliations
- Swinburne University of Technology, Melbourne, Victoria, Australia,
- Swinburne University of Technology, Melbourne, Victoria, Australia
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2009-10
- Volume
- 51
- Issue
- 5
- Pages
- 595-637
- ISSN
- 0018-7208
- 1547-8181
- Cited by
- 56
- References
- 9
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.