Towards a greener Extended-Arrival Manager in air traffic control: A heuristic approach for dynamic speed control using machine-learned delay prediction model. (August 2022) - Zhi Jun, Lim; Alam, Sameer; Dhief, Imen; Schultz, Michael
Abstract: Extended Arrivals Manager (E-AMAN) is a concept that reduces congestion and holding time in the Terminal Maneuver Airspace (TMA) by managing the arrival aircraft during the en-route phase. However, current E-AMAN deployment is only limited to a horizon of 150–200 NM from the airport, restricting the window of opportunity for any early intervention, and the prediction of delay in TMA remains a challenge given the inherent uncertainties in the air traffic environment. In this context, this research work presents an approach for predicting, transferring and absorbing the flight delays and holdings from the highly constrained TMA to the en-route phase using both data-driven and optimisation techniques. First, a method is developed to estimate holding time and TMA delay from historical data. Next, a Machine Learning based prediction framework is developed to predict holdings and delays in the TMA, from an extended horizon of 300–500 NM from the airport. Finally, a heuristics-based optimisation model is developed for dynamic speed management to transfer TMA delays to the en-route phase. To demonstrate the model's efficacy, a case study for Singapore airspace is developed using associated one-day air-traffic data. Four sets of experiments are designed to evaluate the performance of the speed management framework under different flight cooperation levels. For the experiment with the highest number of cooperative flights, the implementation of dynamic speed shows a transfer of 179 min of TMA delay to the en-route phase, equivalent to 65% of the initial TMA delay. This results in an estimated fuel saving of 1524 kg along with a reduction in carbon dioxide emissions of 48000 kg. The findings demonstrate that E-AMAN, for extended horizon with predictive delay modelling and dynamic speed management, has the potential to manage TMA congestion and reduce fuel consumption and emissions, therefore mitigating the environmental impact.