Enabling Interaction-Free Continuous Descent Through Eligible Flight Identification and Top-of-Descent Prediction

When the Top of Descent (TOD) is chosen without accounting for downstream traffic interactions, descents are often interrupted by level-offs to avoid conflicts. To support green aviation practices such as Continuous Descent Operations (CDOs), this paper proposes a three-step learning model to predict TOD locations that enable interaction-free descent trajectories. The first step identifies critical areas–zones with interacting flights that disrupt descents–by analyzing non-CDO flights. The second step employs confident learning on both non-CDO and CDO data to predict CDO-capable flights, addressing label noise to uncover non-CDO flights that could perform a successful CDO. The third step trains a random forest-based model to analyze how interactions with other flights influence TOD decisions, ensuring successful CDOs despite potential conflicts. Experimental results using Air Traffic Management System (ATMS) data from November 2019 in the Singapore Flight Information Region (FIR) demonstrate that predicted TODs eliminate level-offs, increasing average vertical separation by 1418.59 feet. Fuel analysis reveals that interaction-free descents from the predicted TOD save up to 343 kg of fuel for an A330 aircraft, with an average saving of 54.56 kg per flight across major aircraft types. These results highlight the model’s potential for optimizing TOD decisions and promoting sustainable aviation.