Citation

Karikawa D, Aoyama H, Takahashi M, Furuta K, Wakabayashi T, Kitamura M. Cogn. Technol. Work 2013; 15(2): 207-218.

Copyright

(Copyright © 2013, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10111-012-0222-y

PMID

unavailable

Abstract

Improvements of aviation systems are now in progress to ensure the safety and efficiency of air transport in response to the rapid growth of air traffic. For providing theoretical and empirical basis for design and evaluation of aviation systems, researches focusing on cognitive aspects of air traffic controllers are definitely important. Whereas various researches from cognitive perspective have been performed in the Air Traffic Control (ATC) domain, there are few researches trying to illustrate ATCO's control strategies and their effects on task demands in real work situations. The authors believe that findings from these researches can contribute to reveal why ATCOs are capable of handling air traffic safely and efficiently even in the high-density traffic condition. It can be core knowledge for tackling human factors issues in the ATC domain such as development of further effective education and training program of ATCO trainees. However, it is difficult to perform such kinds of researches because identification of ATC task from a given traffic situation and specification of effects of ATCO's control strategies on task demands requires expert knowledge of ATCOs. The present research therefore aims at developing an automated identification and visualization tool of en route ATC tasks based on a cognitive system simulation of an en route controller called COMPAS (COgnitive system Model for simulating Projection-based behaviors of Air traffic controller in dynamic Situations), developed by the authors. The developed visualization tool named COMPASi (COMPAS in interactive mode) equips a projection process model that can simulate realistic features of ATCO's projection involving setting extra margins for errors of projection. The model enables COMPASi to detect ATC tasks in a given traffic situation automatically and to identify Task Demand Level (TDL), that is, an ATC task index. The basic validity of COMPASi has been confirmed through detailed comparison between TDLs given by a training instructor and ones by COMPASi in a simulation-based experiment. Since TDL corresponds to demands of ATC tasks, temporal sequences of TDLs can reflect effectiveness of ATCO's control strategies in terms of regulating task demands. By accumulation and analysis of such kind of data, it may be expected to reveal important aspect of ATCO's skill for achieving the safety and efficiency of air traffic.

Language: en