Classification and quantification of human error in air traffic control: a case study in an airport control tower

Abstract

This study aims at exploring human error in an airport control tower through the technique for the retrospective and predictive analysis of cognitive error (TRACEr) and the controller action reliability assessment (CARA) method. Despite the presence of automated safety nets, air traffic control (ATC) is heavily dependent upon the capabilities of humans. A number of ATC-relevant accidents were characterized by human errors. The data related to error dimensions were collected through interview and direct observation. Then, human error probability and error-producing conditions were evaluated by the CARA method. The results showed that selection and quality, memory, distraction/preoccupation, and traffic and airspace have the highest percentage error rates. Furthermore, the results indicated that the highest probability of error was associated with emergency situation management. This study is the first research to classify and quantify human errors using the TRACEr and the CARA method to evaluate controller error in ATC.