Sanaei, Rasoul and Lau, Alexander and Linke, Florian and Gollnick, Volker (2019) Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations. In: 38th IEEE/AIAA Digital Avionics Systems Conference, DASC 2019. 2019 AIAA/IEEE 38th Digital Avionics Systems Conference (DASC), 2019-09-08 - 2019-09-12, San Diego, California, USA. doi: 10.1109/DASC43569.2019.9081651. ISBN 978-172810649-6. ISSN 2155-7195.
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Abstract
There is a considerable interest in air transportation resilience as a mechanism to cope with the consequences of disruptions to local authorities. Although the identification of metrics and baselines for measuring resilience are still regarded as challenges, we believe that the meaning of disruptions is no longer driven solely by safety threats but also by emergent performance issues. In this paper, resilience of the European Air Traffic Management Network (EATMN) is studied from a performance perspective. In fact, improved predictability and reliability of planning data across the EATMN, allow reduction of reserved Air Traffic Management (ATM) capacity. Consequently, the management of emergent demand-capacity imbalances, regarded as disruptions, is added to tactical phase of Air Fraffic Flow and Capacity Management (ATFCM). In this phase of operations (i.e. day-of-operations) a limited number of variables are available to form aggregated indicators for network resilience. We consider that available ATFCM regulations data reveal restorative mechanisms for tactical Demand-Capacity Balancing (DCB). Aggregated indicators are regarded as enablers to monitor the resilient management of Area Control Centers and to observe spatial distribution of network resiliency. This paper presents an exploratory effort of the needed situational awareness by exploring supervised learning techniques in the context of ATFCM regulations to predict Air Traffic Flow Management (ATFM) delay. In particular, it focuses on the application of machine learning algorithms and comparison of different architecture variants to a regression study on tactical DCB disruptions.
Item URL in elib: | https://elib.dlr.de/126926/ | ||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech, Other) | ||||||||||||||||||||
Title: | Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations | ||||||||||||||||||||
Authors: |
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Date: | 2019 | ||||||||||||||||||||
Journal or Publication Title: | 38th IEEE/AIAA Digital Avionics Systems Conference, DASC 2019 | ||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||
Open Access: | Yes | ||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||
DOI: | 10.1109/DASC43569.2019.9081651 | ||||||||||||||||||||
ISSN: | 2155-7195 | ||||||||||||||||||||
ISBN: | 978-172810649-6 | ||||||||||||||||||||
Status: | Published | ||||||||||||||||||||
Keywords: | Network Resilience, ATFCM regulations, Emergent disruptions, Machine Learning, Neural networks, Supervised Learning | ||||||||||||||||||||
Event Title: | 2019 AIAA/IEEE 38th Digital Avionics Systems Conference (DASC) | ||||||||||||||||||||
Event Location: | San Diego, California, USA | ||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||
Event Start Date: | 8 September 2019 | ||||||||||||||||||||
Event End Date: | 12 September 2019 | ||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||
HGF - Program: | Aeronautics | ||||||||||||||||||||
HGF - Program Themes: | air traffic management and operations | ||||||||||||||||||||
DLR - Research area: | Aeronautics | ||||||||||||||||||||
DLR - Program: | L AO - Air Traffic Management and Operation | ||||||||||||||||||||
DLR - Research theme (Project): | L - Air Traffic Concepts and Operation (old) | ||||||||||||||||||||
Location: | Hamburg | ||||||||||||||||||||
Institutes and Institutions: | Air Transport Operations > Air Transport Infrastructures & Processes | ||||||||||||||||||||
Deposited By: | Sanaei, Rasoul | ||||||||||||||||||||
Deposited On: | 08 Apr 2019 09:19 | ||||||||||||||||||||
Last Modified: | 24 Apr 2024 20:30 |
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