Unearthing Decisional Patterns of Air Traffic Control Officers from Simulator Data
Authors: Z. Zakaria, S. W. Lye, S. Endy
Abstract:
Despite the continuous advancements in automated conflict resolution tools, there is still a low rate of adoption of automation from Air Traffic Control Officers (ATCOs). Trust or acceptance in these tools and conformance to the individual ATCO preferences in strategy execution for conflict resolution are two key factors that impact their use. This paper proposes a methodology to unearth and classify ATCO conflict resolution strategies from simulator data of trained and qualified ATCOs. The methodology involves the extraction of ATCO executive control actions and the establishment of a system of strategy resolution classification based on ATCO radar commands and prevailing flight parameters in deconflicting a pair of aircraft. Six main strategies used to handle various categories of conflict were identified and discussed. It was found that ATCOs were about twice more likely to choose only vertical maneuvers in conflict resolution compared to horizontal maneuvers or a combination of both vertical and horizontal maneuvers.
Keywords: Air traffic control strategies, conflict resolution, simulator data, strategy classification system.
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[1] H. Winter, “Automation of conflict detection and resolution in ATM,” Aerospace science and technology, 7(6), 2003. W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] H. Erzberger, “Automated conflict resolution for air traffic control,” National Aeronautics and Space Administration, pp. 3946-3673, Ames Research Center, 2005.
[3] P. N. Tran, D. T. Pham, S. K. Goh, S. Alam, and V. Duong, “An interactive conflict solver for learning air traffic conflict resolutions,” Journal of Aerospace Information Systems, 17(6), 271-277, 2020.
[4] M. Brittain, and P. Wei, “Autonomous air traffic controller: A deep multi-agent reinforcement learning approach,” arXiv preprint arXiv:1905.01303, 2019.
[5] C. Westin, B. Hilburn, C. Borst, and D. Schaefer, "The effect of strategic conformance on acceptance of automated advice: concluding the MUFASA project," Proceedings of the SESAR Innovation Days, Vol. 3, 2013.
[6] C. Westin, C. Borst, and B. Hilburn, "Automation Transparency and Personalized Decision Support: Air Traffic Controller Interaction with a Resolution Advisory System," IFAC-PapersOnLine, Vol. 49, No. 19, pp. 201–206, 2016.
[7] A. Nunes, and R. H. Mogford, “Identifying Controller Strategies that Support the 'Picture.',” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 47(1), 71–75, 2003.
[8] E. M. Rantanen, and A. Nunes, “Hierarchical conflict detection in air traffic control,” The International Journal of Aviation Psychology, 15(4), 339-362, 2005.
[9] K. Eyferth, C. Niessen, and O. Spaeth, “A model of air traffic controllers' conflict detection and conflict resolution,” Aerospace science and technology, 7(6), 409-416, 2003.
[10] E. M. Rantanen, and C. D. Wickens, “Conflict resolution manoeuvres in air traffic control: Investigation of operational data,” The International Journal of Aviation Psychology, 22(3), 266-281, 2012.
[11] International Civil Aviation Organisation (ICAO), Air Traffic Management (DOC 4444), 2016.
[12] H. J. Wee, S. W. Lye, and J. P. Pinheiro, “An integrated highly synchronous, high resolution, real time eye tracking system for dynamic flight movement,” Advanced Engineering Informatics, 41, 100919, 2019.
[13] A. Bisseret and J. Leplat, “Analysis of the processes involved in the treatment of information by the air traffic controller (Air traffic controller performance in detecting possible conflicts among aircraft),” The Controller, 5, 13-22, 1966.
[14] H. Helbing, “A Cognitive Model of En-Route Air Traffic Control,” IFAC Proceedings Volumes, 30(24), 57-60, 1997.
[15] Federal Aviation Administration. (2021, May 5). JO 7110.65Z - Air Traffic Control Document Information Share Print. Retrieved January 5, 2022, from https://www.faa.gov/regulations_policies/orders_notices/index.cfm/go/document.information/documentID/1039720
[16] J. Kuchar, and L. C. Yang, “A Review of Conflict Detection and Resolution Modeling Methods,” Intelligent Transportation Systems, IEEE Transactions on. 1. 179 – 189, 2001