<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/F6340624-B652-4DC3-B8B3-9B0E93747795" ns1:id="F6340624-B652-4DC3-B8B3-9B0E93747795"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/9F2A53E4-4E00-489C-9FDB-43DDAC995F69" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/73475AE7-7FAD-4A63-ABA8-35F8096AC564" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/73475AE7-7FAD-4A63-ABA8-35F8096AC564" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-05-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/EEEB367D-CF14-4460-A675-3CFE0CA170FA" ns1:rel="FUND" ns1:start="2023-05-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10088428</ns2:identifier></ns2:identifiers><ns2:title>INTELLIGENT SUITE FOR LOCAL AND NETWORK DEMAND AND CAPACITY BALANCING</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>EU-Funded</ns2:grantCategory><ns2:leadFunder>Horizon Europe Guarantee</ns2:leadFunder><ns2:abstractText>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 addressesthe R&amp;amp;I need for on-demand air traffic servicesreflective 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.</ns2:abstractText></ns2:project>