Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152087
Title: Unstable approach in aviation : mental model disconnects between pilots and air traffic controllers and interaction conflicts
Authors: Lai, Hsueh-Yi
Chen, Chun-Hsien
Khoo, Li Pheng
Zheng, Pai
Keywords: Engineering::Aeronautical engineering
Issue Date: 2019
Source: Lai, H., Chen, C., Khoo, L. P. & Zheng, P. (2019). Unstable approach in aviation : mental model disconnects between pilots and air traffic controllers and interaction conflicts. Reliability Engineering and System Safety, 185, 383-391. https://dx.doi.org/10.1016/j.ress.2019.01.009
Project: IRB-2015-08-009
ATMRI: 2014-R5-CHEN
Journal: Reliability Engineering and System Safety
Abstract: Landing safety has long been a critical issue. To examine how system failures occur, the shared mental models (SMMs) between pilots and air traffic controllers (ATCOs) are studied. Meanwhile, to understand the context of various mental model disconnects, different categories of SMMs are also examined. Nine pilots and seven ATCOs are recruited to conduct the crew-based critical decision method. The SMMs of both parties are compared to identify the mental model disconnects associated with their interaction conflicts during landings. A key discovery of this research is that the mental model disconnects in task-related SMMs made it difficult to integrate various working results, which requires extra interaction to mitigate the contradiction in requirements. Furthermore, the conflicting disconnects caused by various priorities in a heterogeneous system are also identified, which requires further negotiation between the parties to achieve a mutually beneficial outcome. In team-related SMMs, mental model disconnects make different parties in a system hard to coordinate the respective requirements of members and also impede the prevention of system risks, both of which cause the continuation of risky operations. Finally, coordination of these SMMs forms a robust loop in which a system can actively detect and address the potential risks.
URI: https://hdl.handle.net/10356/152087
ISSN: 0951-8320
DOI: 10.1016/j.ress.2019.01.009
Rights: © 2019 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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