{"id":15,"date":"2025-02-05T15:41:11","date_gmt":"2025-02-05T15:41:11","guid":{"rendered":"https:\/\/research.dblue.it\/tada\/?page_id=15"},"modified":"2025-04-09T11:33:20","modified_gmt":"2025-04-09T10:33:20","slug":"homepage","status":"publish","type":"page","link":"https:\/\/research.dblue.it\/tada\/","title":{"rendered":"Homepage"},"content":{"rendered":"

<\/div><\/div><\/div><\/div>

Overview<\/h3><\/div>

The TADA project is <\/span><\/span>pioneering the integration of Artificial Intelligence <\/span>(AI) <\/span>into Air Traffic Management to enhance <\/span>predictability, <\/span>efficiency,<\/span> sustainability,<\/span> safety, <\/span>and <\/span>capacity<\/span> in Terminal Manoeuvring Areas (TMAs)<\/span><\/span><\/strong>. These high-traffic zones, especially around major airports, face increasing <\/span>challenges<\/span> in traffic complexity<\/span>. <\/span>TADA<\/span> will support Air Traffic Control <\/span>Officers<\/span> (ATCOs) <\/span>to better handle incoming aircrafts at the airport<\/span>s<\/span>.\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n

TADA <\/span>makes use of<\/span> M<\/span>achine <\/span>L<\/span>earning and big data to optimi<\/span>s<\/span>e decision-making in air traffic control (ATC). By <\/span>analysing<\/span> ATCO interactions and patterns, the project aims to <\/span><\/span>enhance automation, improve trajectory predictions, and <\/span>provide innovations to decision support<\/span> tools<\/span><\/span><\/strong>, such as <\/span>Arrival Management Systems (AMAN). Through these advancements, TADA is shaping the future of smarter, more <\/span>efficient<\/span> and <\/span>adapt<\/span>ive air traffic management.<\/span><\/span>\u00a0<\/span><\/span>
<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div>

TADA Objectives<\/h3><\/div><\/div><\/div>
OBJECTIVE 1<\/h5><\/div>

Develop an AI-powered Digital Assistant based on historical ATC<\/span> data to support decision-making to achieve <\/span>the desired arrival<\/span> sequences. Linked to this <\/span>objective<\/span> there is also the development <\/span>of AI modules to support AMAN in proposing the best <\/span>sequence<\/span>. The tool aims to reduce <\/span>ATCO <\/span>workloads while<\/span> increasing <\/span>capacity<\/span>, <\/span>safety<\/span> and sustainability of operations.<\/span><\/span>\u00a0<\/span><\/p>\n<\/div><\/div><\/div>

OBJECTIVE 2<\/h5><\/div>

Develop an innovative Human-Machine Interface (HMI) based on the EASA AI Framework<\/a><\/strong><\/span> to allow an enhanced Human-AI teaming between ATCOs and TADA\u2019s digital tool. The TADA HMI study will also consider how the TADA Solution\u2019s inputs are presented and apply Explainable AI (XAI).<\/p>\n<\/div><\/div><\/div>

OBJECTIVE 3<\/h5><\/div>

Validate TADA and the associated HMI concepts by conducting early testing through Human-in-the-loop (HITL) validation with operational staff involved in TMA Air Traffic Control operations.\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n<\/div><\/div><\/div>

OBJECTIVE 4<\/h5><\/div>

Gain further understanding of the best Human-AI teaming in ATC between support for decision, action selection, and autonomous ATC under human monitoring (Automation Lv. 4 maximum) by performing a comparison study during the project and disseminate the results.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

The project in numbers<\/h3><\/div><\/div><\/div>
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Months<\/h3><\/div><\/div><\/div>
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Partners<\/h3><\/div><\/div><\/div>
<\/span><\/div>

Countries<\/h3><\/div><\/div><\/div><\/div><\/div>

Timeline<\/h3><\/div>
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