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Extracting Capacity Metrics for Gene...

Mitkas, Danae Zoe.

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Extracting Capacity Metrics for General Aviation Airports from ADS-B Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Extracting Capacity Metrics for General Aviation Airports from ADS-B Data./
Author:	Mitkas, Danae Zoe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	44 p.
Notes:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
Subject:	Civil engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28492053
ISBN:	9798534677331

Extracting Capacity Metrics for General Aviation Airports from ADS-B Data.
Mitkas, Danae Zoe.

Extracting Capacity Metrics for General Aviation Airports from ADS-B Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 44 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--University of Maryland, College Park, 2021.

General Aviation airports play a pivotal role in the aviation system of the US, with over 5000 small airports existing and operating across the country. Serving almost exclusively small aircraft, these airports have a unique activity profile, compared to larger commercial airports. Like their larger cousins, they occasionally see the need to apply for federal funding for capacity enhancement projects, a process that requires rigorous documentation of the demand and capacity situation at the airport. Existing models for capacity estimation have been calibrated to reflect the much larger scale features that dominate large airports. The main challenge is to develop a method to provide precise data for small airports that operate mainly with small single or multi-engine aircraft. These airports are typically not towered and, hence, do not benefit from traditional automated data collection technologies. This research addresses the issues of a) collecting aircraft data at local airport environments from aircraft equipped with Automated Dependent Surveillance - Broadcast (ADS-B) technology, b) processing the data to determine and classify flights, and c) assessing elements of the operational performance of these aircraft. The thesis proposes a method to extract aircraft approach speeds and runway occupancy times, which are important contributors to capacity estimation. We applied and validated our method in three small airports.

ISBN: 9798534677331Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

ADS-B

Extracting Capacity Metrics for General Aviation Airports from ADS-B Data.
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500 $a Source: Masters Abstracts International, Volume: 83-02.
500 $a Advisor: Lovell, David J.
502 $a Thesis (M.S.)--University of Maryland, College Park, 2021.
520 $a General Aviation airports play a pivotal role in the aviation system of the US, with over 5000 small airports existing and operating across the country. Serving almost exclusively small aircraft, these airports have a unique activity profile, compared to larger commercial airports. Like their larger cousins, they occasionally see the need to apply for federal funding for capacity enhancement projects, a process that requires rigorous documentation of the demand and capacity situation at the airport. Existing models for capacity estimation have been calibrated to reflect the much larger scale features that dominate large airports. The main challenge is to develop a method to provide precise data for small airports that operate mainly with small single or multi-engine aircraft. These airports are typically not towered and, hence, do not benefit from traditional automated data collection technologies. This research addresses the issues of a) collecting aircraft data at local airport environments from aircraft equipped with Automated Dependent Surveillance - Broadcast (ADS-B) technology, b) processing the data to determine and classify flights, and c) assessing elements of the operational performance of these aircraft. The thesis proposes a method to extract aircraft approach speeds and runway occupancy times, which are important contributors to capacity estimation. We applied and validated our method in three small airports.
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