Quantitative analysis method of ATCO's error risks based on TRACEr

doi:10.16265/j.cnki.issn1003-3033.2020.08.016

Abstract

Abstract: In order to accurately identify human error risks of ATCO and quantitatively predict their probability in different cognitive domains, error types of ATCO were defined in accordance with cognitive functional framework of TRACEr, and causing factors for psychological error mechanism were analyzed before a Bayesian network of unsafe incidents in Air Traffic Management (ATM) was established with such factors as root nodes. Then, conditional probabilities of network nodes were calculated by introducing weighted distance, and human error data of unsafe incidents in China during last decade were analyzed to investigate risk factors. The results show that ATCO's errors could be generated in any cognitive domain, which leads to a probability of 0.040 7 of unsafe incidents. Among all factors, alertness failure, insufficient learning, neglecting danger, miscommunication and unclear convey of information are major ones, which results in a higher probability of judgement error and information transmission error.

Key words: technique for retrospective and predictive analysis of cognitive errors(TRACEr), air traffic controller(ATCO), human error risks, cognitive domain, Bayesian network

CLC Number:

X949

YANG Yue, SONG Xiangbo, WANG Jianzhong. Quantitative analysis method of ATCO's error risks based on TRACEr[J]. China Safety Science Journal, 2020, 30(8): 109-115.

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