Prediction of major air traffic flows using ADS-B data

Prediction of air traffic flow, i.e., staggering the air traffic demand over time and space, is a very important activity in Air Traffic Flow Management (ATFM). Accurate air traffic flow prediction can advise ATFM about the forthcoming air traffic in the airspace and help ATFM develop control st...

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Main Author: Tanush, Seshadri
Other Authors: Sameer Alam
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/159020
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1590202023-03-04T20:18:57Z Prediction of major air traffic flows using ADS-B data Tanush, Seshadri Sameer Alam School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute sameeralam@ntu.edu.sg Engineering::Aeronautical engineering::Aviation Prediction of air traffic flow, i.e., staggering the air traffic demand over time and space, is a very important activity in Air Traffic Flow Management (ATFM). Accurate air traffic flow prediction can advise ATFM about the forthcoming air traffic in the airspace and help ATFM develop control strategies in advance to address anticipated saturations in the airspace. There are mainly two ways for air traffic flow prediction. One way is propagating the trajectories of flights forward in time and counting the number of aircrafts at a particular sector in the airspace. While making accurate predictions, it does so for a duration of up to 20 minutes which is far from ideal for ATFM. The other way is the aggregated flow prediction which provides the distribution of traffic flows in the airspace. One of the state-of-the-art aggregate prediction approaches is the Linear Dynamic System Model (LDSM) which: i) predicts air traffic flow for a whole day in advance based on historical data, ii) Accounts for uncertainty in departure. iii) makes predictions based on number aircrafts in sector in previous time interval is ideal. The LSTM model has been proposed and applied on the American Airspace. However, it remains to be seen if it can do so for other airspaces. This report aims to examine the accuracy of air traffic flow prediction using the LDSM model and analyze the potential influencing factors of the prediction accuracy. Based on the flight trajectory data, this report has carried out a case study in the Singapore airspace. Results show that the prediction of air traffic flow for the Singapore airspace is not as accurate as the prediction for the American airspace Bachelor of Engineering (Mechanical Engineering) 2022-06-09T01:36:32Z 2022-06-09T01:36:32Z 2022 Final Year Project (FYP) Tanush, S. (2022). Prediction of major air traffic flows using ADS-B data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159020 https://hdl.handle.net/10356/159020 en A149 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Aeronautical engineering::Aviation
spellingShingle Engineering::Aeronautical engineering::Aviation
Tanush, Seshadri
Prediction of major air traffic flows using ADS-B data
description Prediction of air traffic flow, i.e., staggering the air traffic demand over time and space, is a very important activity in Air Traffic Flow Management (ATFM). Accurate air traffic flow prediction can advise ATFM about the forthcoming air traffic in the airspace and help ATFM develop control strategies in advance to address anticipated saturations in the airspace. There are mainly two ways for air traffic flow prediction. One way is propagating the trajectories of flights forward in time and counting the number of aircrafts at a particular sector in the airspace. While making accurate predictions, it does so for a duration of up to 20 minutes which is far from ideal for ATFM. The other way is the aggregated flow prediction which provides the distribution of traffic flows in the airspace. One of the state-of-the-art aggregate prediction approaches is the Linear Dynamic System Model (LDSM) which: i) predicts air traffic flow for a whole day in advance based on historical data, ii) Accounts for uncertainty in departure. iii) makes predictions based on number aircrafts in sector in previous time interval is ideal. The LSTM model has been proposed and applied on the American Airspace. However, it remains to be seen if it can do so for other airspaces. This report aims to examine the accuracy of air traffic flow prediction using the LDSM model and analyze the potential influencing factors of the prediction accuracy. Based on the flight trajectory data, this report has carried out a case study in the Singapore airspace. Results show that the prediction of air traffic flow for the Singapore airspace is not as accurate as the prediction for the American airspace
author2 Sameer Alam
author_facet Sameer Alam
Tanush, Seshadri
format Final Year Project
author Tanush, Seshadri
author_sort Tanush, Seshadri
title Prediction of major air traffic flows using ADS-B data
title_short Prediction of major air traffic flows using ADS-B data
title_full Prediction of major air traffic flows using ADS-B data
title_fullStr Prediction of major air traffic flows using ADS-B data
title_full_unstemmed Prediction of major air traffic flows using ADS-B data
title_sort prediction of major air traffic flows using ads-b data
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/159020
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