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Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations

Sanaei, Rasoul and Lau, Alexander and Linke, Florian and Gollnick, Volker (2019) Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations. 2019 AIAA/IEEE 38th Digital Avionics Systems Conference (DASC), 08-12.Sep.2019, San Diego, California, USA. doi: 10.1109/DASC43569.2019.9081651.

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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/
Document Type:Conference or Workshop Item (Speech, Other)
Title:Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sanaei, RasoulRasoul.Sanaei (at) dlr.dehttps://orcid.org/0000-0001-7063-5114
Lau, AlexanderAlexander.Lau (at) dlr.dehttps://orcid.org/0000-0001-6150-6169
Linke, FlorianFlorian.Linke (at) dlr.dehttps://orcid.org/0000-0003-1403-3471
Gollnick, VolkerVolker.Gollnick (at) dlr.dehttps://orcid.org/0000-0001-7214-0828
Date:2019
Refereed publication:Yes
Open Access:Yes
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/DASC43569.2019.9081651
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 Dates:08-12.Sep.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:22 Jan 2021 11:06

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