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|Title:||Factor studies into the assessment of ATCO's level of comprehension of traffic scenarios||Authors:||Goh, Brendon Kai Lun||Keywords:||Engineering::Aeronautical engineering::Accidents and air safety||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Goh, B. K. L. (2022). Factor studies into the assessment of ATCO's level of comprehension of traffic scenarios. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158640||Abstract:||There is a need to improve current air traffic management systems to cope with the growing airspace demand. The ATCO’s situational awareness is essential in ensuring safe flight operations in the airspace, but it is limited by human factors. Technological advancements have enabled the use of physiological measures such as eye tracking to study situational awareness. The findings could be used to develop future air traffic management systems where technologies and systems can make use of eye tracking to understand the ATCO’s situational awareness and assist in the ATC operations. Currently the relationship between an ATCO’s eye tracking metrics and his comprehension of an airspace situation has yet to be established. Hence, this study aims to investigate the relationship between the ATCO’s eye tracking behaviour and comprehension of the air traffic scenario, and to develop a model to predict and characterise the comprehension level base on the eye tracking behaviour exhibited by the ATCO. The study will focus on sector boundary management, conflict detection and holding management as the types of scenarios with varying difficulty levels. Experiments are conducted for each scenario type, where the eye tracking data and scenario comprehension level of the participants will be recorded. These data would be analysed to establish a relationship and develop a model to characterise comprehension base on eye tracking metrics and the scenario difficulty level. The models to predict comprehension were successfully developed for each scenario with an accuracy of 80%, 69% and 67% for sector boundary management, conflict detection and holding management respectively. However, the sample size is small, and more validation studies could be conducted to ensure the accuracy of the comprehension prediction models.||URI:||https://hdl.handle.net/10356/158640||Schools:||School of Mechanical and Aerospace Engineering||Research Centres:||Air Traffic Management Research Institute||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
Updated on Nov 29, 2023
Updated on Nov 29, 2023
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