Machine learning automation of target off-block time
08 Apr 2025
N111
Management and operations – increasing capacity and efficiency, airside
Airports utilizing collaborative decision-making (A-CDM) rely on accurate estimates of target off-block time (TOBT) set by airlines or ground handlers. This time indicates the end of ground handling, including possible ground delays, and should be updated whenever changes in the handling progress occur. Last-minute changes in TOBT can disrupt pre-departure sequence and air traffic flow management, making its timely and accurate estimate vital. As one of the EATIN (Eurocontrol Air Transport Innovation Network) initiatives, this work presents a machine learning model for TOBT predictions, their implementation into Prague Airport’s operating system and their distribution to stakeholders and the network manager.
- A machine learning model for turnaround and target off-block time predictions
- The validation steps and integration of machine learning into Prague Airport's operating system
- The approach used at Prague Airport to automatically distribute the model's outcome to stakeholders and the network manager
- An analysis showing the acceptance of the model's outcome by ground handlers at Prague Airport
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