Abstract
Losses of separation (LoS) are breaches of regulations that specify the minimum distance between aircraft in controlled airspace. Erroneous communications between air traffic controllers (ATCs) and pilots are leading contributors to LoS that result in elevated risk of fatal accidents. An air traffic control system that could identify communication errors promptly would, therefore, be advantageous. Establishing such a system requires a systematic characterization of communication errors to reveal how various communication arrangements and errors influence the development of LoS. Such know-how could guide the ATCs and pilots in identifying the parts of their communication processes and content that most influence the occurrence of LoS. Existing studies of LoS focus on simulation of aircraft operation processes with little quantitative analysis about how communication issues arise and result in elevated risks of LoS. This paper presents a method for supporting automatic communication error detection through integrated use of speech recognition, text analysis, and formal modeling of airport operational processes. The proposed method focuses on: identifying communication features to guide the detection of vulnerable communications; characterizing communication errors; and Bayesian Network modeling for predicting communication errors and LoS using the features derived from ATC–pilot communications. Major findings show that incorrect read-backs by pilots are highly correlated with a majority of LoS. Results indicate the proposed method could form a basis for automating communication error detection and preventing LoS. The integrated Automatic Speech Recognition and Natural Language Processing functions may be incorporated into existing aviation applications for real-time ATC–pilot communication monitoring and preventive LoS control.
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