Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77463
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dc.contributor.authorThanaraj, T.
dc.date.accessioned2019-05-29T06:32:28Z
dc.date.available2019-05-29T06:32:28Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77463
dc.description.abstractAir traffic control (ATC) communication is an important link between pilots and controllers. Often, ATC controllers experience immense pressure when the airspace sector they are handling becomes more complex. Miscommunication in ATC communication could lead to accidents, costing lives or damage to property. This project measured the influence of factors affecting an airport’s operational environment, such as weather and flight arrival sequence, on ATC communication between pilot and controllers. This project focused on developing a machine learning technique to identify active rate, an important feature in ATC communication which measures amount of communication for a period of time. With the help of data analysis, strong correlation was identified between flight trajectory data and active rate. It was determined that anomalous flight trajectories increased ATC communication by 28%. Henceforth, a machine learning prediction model was developed to identify anomalous flight trajectory in real-time, using which an increase in ATC communication can be predicted.en_US
dc.format.extent68 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Aeronautical engineeringen_US
dc.titleFeature extraction from pilot-controller voice communication using machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSameer Alamen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Aerospace Engineering)en_US
dc.contributor.organizationAir Traffic Management Research Instituteen_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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