Exploitation of Machine Learning to predict airport Runway Utilisation relative to known precursors and abnormality

F.F. Herrema

doi:10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546

Exploitation of Machine Learning to predict airport Runway Utilisation relative to known precursors and abnormality

F.F. Herrema

Air Transport & Operations

Research output: Thesis › Dissertation (TU Delft)

142 Downloads (Pure)

Abstract

There is currently no supplementary operational system that assists the Air Traffic Control Officer (ATCO) in attaining accurate, fast, intuitive and interpretable predictions of Aircraft Safety Performance (ASP) enablers through suitable visualisation on the runway or on final approach. Thus, this study intends to develop an arrival ATCO support decision tool named the Runway Utilisation (RU) support tool.

Original language	English
Awarding Institution	Delft University of Technology
Supervisors/Advisors	Curran, Richard, Supervisor Santos, B.F., Advisor
Award date	4 Mar 2020
DOIs	https://doi.org/10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546
Publication status	Published - 2020

Access to Document

10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546

Report PhD Floris Herrema FinalFinal published version, 4.32 MB

Cite this

@phdthesis{e96d5b2cf0ce4cbeb1c4bf5c408ad546,

title = "Exploitation of Machine Learning to predict airport Runway Utilisation relative to known precursors and abnormality",

abstract = "There is currently no supplementary operational system that assists the Air Traffic Control Officer (ATCO) in attaining accurate, fast, intuitive and interpretable predictions of Aircraft Safety Performance (ASP) enablers through suitable visualisation on the runway or on final approach. Thus, this study intends to develop an arrival ATCO support decision tool named the Runway Utilisation (RU) support tool.",

author = "F.F. Herrema",

year = "2020",

doi = "10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546",

language = "English",

type = "Dissertation (TU Delft)",

school = "Delft University of Technology",

}

TY - THES

T1 - Exploitation of Machine Learning to predict airport Runway Utilisation relative to known precursors and abnormality

AU - Herrema, F.F.

PY - 2020

Y1 - 2020

N2 - There is currently no supplementary operational system that assists the Air Traffic Control Officer (ATCO) in attaining accurate, fast, intuitive and interpretable predictions of Aircraft Safety Performance (ASP) enablers through suitable visualisation on the runway or on final approach. Thus, this study intends to develop an arrival ATCO support decision tool named the Runway Utilisation (RU) support tool.

AB - There is currently no supplementary operational system that assists the Air Traffic Control Officer (ATCO) in attaining accurate, fast, intuitive and interpretable predictions of Aircraft Safety Performance (ASP) enablers through suitable visualisation on the runway or on final approach. Thus, this study intends to develop an arrival ATCO support decision tool named the Runway Utilisation (RU) support tool.

U2 - 10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546

DO - 10.4233/uuid:e96d5b2c-f0ce-4cbe-b1c4-bf5c408ad546

M3 - Dissertation (TU Delft)

ER -

Exploitation of Machine Learning to predict airport Runway Utilisation relative to known precursors and abnormality

Abstract

Access to Document

Fingerprint

Cite this