Descrizione del progetto
Un nuovo strumento per la gestione del traffico aereo
La congestione del traffico aereo continua a rappresentare una sfida per l’industria aeronautica, nonostante l’implementazione di misure per gestirne il flusso e la capacità. Basate sui dati relativi ai piani di volo, queste misure non tengono conto delle traiettorie effettive realizzate dagli aeromobili, per cui i controllori del traffico aereo (CTA) dispongono di tempo limitato per risolvere i problemi nei punti maggiormente interessati, causando ritardi e un aumento del carico di lavoro. In questo contesto, il progetto ASTRA, finanziato dall’UE, mira a sviluppare uno strumento basato sull’intelligenza artificiale in grado di prevedere e risolvere i punti caldi del traffico aereo molto prima di quanto consentito dai metodi attualmente a disposizione. In particolare, il personale che si occupa della posizione di gestione del flusso acquisirà la capacità di prevedere gli hotspot con almeno un’ora di anticipo, consentendo di proporre conseguentemente strategie di risoluzione. Grazie all’incremento della capacità, al rispetto di traiettorie efficienti, alla riduzione del carico di lavoro dei CTA e alla prevedibilità delle operazioni, questo strumento apporta benefici molto significativi.
Obiettivo
Various Air Traffic Flow and Capacity Management (ATFCM) measures are implemented during the pre-tactical and tactical flow management phases to resolve traffic congestion (aka hotspots); however, these are generally based on flight plan data. On the day of operation, an aircraft's actual trajectory may differ significantly from its flight plan and, as a result, hotspots still occur and these have to be resolved by Air Traffic Controllers (ATCOs) without sufficient advance notice. With today's ATFCM tools, tactical Air Traffic Control (ATC) hotspots are only identified up to around 20 minutes in advance. The aim of ASTRA is to bridge the gap between the Flow Management Position (FMP) and the planner Controller Working Position (CWP) by developing a AI-based tool (to TRL2) for FMP personnel which can predict and resolve hotspots earlier than today, before they are within the scope of the sector planner. The objectives of the project are to: (a) develop an FMP function to predict hotspots at least 1 hour in advance, and to propose strategies to resolve them; (b) develop Human Machine Interface (HMI) concepts to allow interaction between operators and the tool; and (c) demonstrate and validate the tool by conducting human-in-the-loop Real-Time Simulations (RTS) in a representative operational environment. The benefits of this tool would include: increased capacity at ATC unit level; better adherence to efficient and green business trajectories; reduced ATCO workload; and more predictable operations. The project will be carried out by a multidisciplinary consortium of 5 partners from 4 countries - Malta, Spain, Italy and Switzerland - including an academic institution, an ANSP, an Air Traffic Management (ATM) technology provider, and two consulting/research entities. The partners are complementary to each other and bring a combination of academic, technical, human factor and operational skills and expertise to the project.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.2.5 - Climate, Energy and Mobility Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-JU-RIA - HORIZON JU Research and Innovation ActionsCoordinatore
2080 MSIDA
Malta