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Predicting Deviation of Flight Entry into Air Sector using Machine Learning Techniques

Klotergens, Christian and Acevedo, Cristina and Firmansyah, Indra and Antiqui, Leonardo and Madhusudhanan, Kiran and Jameel, Mohsan (2023) Predicting Deviation of Flight Entry into Air Sector using Machine Learning Techniques. In: 42nd IEEE/AIAA Digital Avionics Systems Conference, DASC 2023. 42nd AIAA/IEEE Digital Avionics Systems Conference (DASC), 2023-10-01 - 2023-10-05, Barcelona, Spain. doi: 10.1109/DASC58513.2023.10311263. ISBN 979-835033357-2. ISSN 2155-7195.

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Abstract

The management of air traffic is a complex task that requires ensuring the safety and efficiency of aircraft trajectories when transiting from one airspace sector into another. This work explores the use of historical flight data to predict if a flight will commit to the planned entry point when entering an airspace sector. To achieve this, we propose a feature engineering method that can be employed to convert raw flight data into a matrix which captures flight count information in predefined grids. This matrix is referred to as the Air Space Occupancy Grid (ASOG) and it captures the state of traffic in an airspace sector and its immediate vicinity. Experiments are performed using the Swedish Civil Air Traffic Control (SCAT) dataset. To predict whether an aircraft will deviate from its planned entry point, supervised machine learning algorithms are used to train a model. Through experiments on real-world data, we showcase that ASOG provides a systematic way of incorporating the state of the airspace sector and improving the performance of prediction models compared to simple features. The prediction output can be used to notify human air traffic controllers in advance about potential deviation to flight plan upon entry to an airspace sector. This can improve the planning process of air traffic controllers in their work in maintaining safe and efficient air traffic.

Item URL in elib:https://elib.dlr.de/199005/
Document Type:Conference or Workshop Item (Lecture, Speech)
Title:Predicting Deviation of Flight Entry into Air Sector using Machine Learning Techniques
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Klotergens, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Acevedo, CristinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Firmansyah, IndraUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Antiqui, LeonardoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Madhusudhanan, KiranUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jameel, MohsanFL-LASUNSPECIFIEDUNSPECIFIED
Date:1 October 2023
Journal or Publication Title:42nd IEEE/AIAA Digital Avionics Systems Conference, DASC 2023
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/DASC58513.2023.10311263
ISSN:2155-7195
ISBN:979-835033357-2
Status:Published
Keywords:Prediction, digital Assistant, en-route, Machine Learning, Data Analysis
Event Title:42nd AIAA/IEEE Digital Avionics Systems Conference (DASC)
Event Location:Barcelona, Spain
Event Type:international Conference
Event Start Date:1 October 2023
Event End Date:5 October 2023
Organizer:AIAA/ IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Air Transport Operations and Impact Assessment, L - Integrated Flight Guidance
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Controller Assistance
Deposited By: Jameel, Mohsan
Deposited On:15 Nov 2023 11:11
Last Modified:24 Apr 2024 20:59

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