Project description
AI and machine learning for on-demand air traffic services
Air traffic management (ATM) plays a vital role in delivering safe and efficient air transport services. However, the current demands of air transport call for an improved cost-efficiency of air traffic services provision, while maintaining safety. The EU-funded ISLAND project will conduct industrial research that streamlines the timely and efficient creation and use of airspace capacity. Project work focuses on the implementation of advanced levels of dynamic airspace configuration, employing various virtualisation models, digital INAP applications and network-wide monitoring with a high degree of automation. The project harnesses the power of artificial intelligence and machine learning to provide on-demand air traffic services that can flexibly adapt to meet traffic demands, ensuring a continuous and uninterrupted provision of ATM services.
Objective
The Project encompasses the industrial research aimed to timely and efficiently create and use airspace capacity, in combination with targeted, effective demand and/or capacity measures. As such, it will focus on advanced levels of dynamic airspace configuration, Leveraging different virtualization models, digital INAP applications as well as Network-wide monitoring, all with high levels of automation. The project addresses the R&I need for on-demand air traffic services reflective of traffic demand, and the continuity of ATM service despite disruption. The project exploits the latest advancements in artificial intelligence and machine learning, to supply a variety of supporting toolsets to ATM stakeholders that enable rapid exploration of options for the deployment of capacity-on-demand solutions, whenever and wherever required. The benefits include increased en-route capacity and improved cost-efficiency of ATS provision, without compromising the current safety levels.
Fields of science
Keywords
Programme(s)
- HORIZON.2.5 - Climate, Energy and Mobility Main Programme
Funding Scheme
HORIZON-JU-RIA - HORIZON JU Research and Innovation ActionsCoordinator
1130 Bruxelles / Brussel
Belgium
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Participants (17)
1059 CM Amsterdam
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75720 Paris
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28022 Madrid
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
28036 Madrid
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
28022 Madrid
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28108 Alcobendas Madrid
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51147 Koln
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91120 Palaiseau
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1030 Wien
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10410 Velika Gorica
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602 27 Norrkoping
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1700 008 Lisboa
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2770 Kastrup
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78990 Elancourt
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00155 Roma
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
63225 Langen
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D02 T449 DUBLIN
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Partners (1)
PO15 7FL Fareham
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