Aircraft Landing Time Prediction with Deep Learning on Trajectory Images

TL;DR

This work tackles aircraft landing time (ALT) prediction by transforming TMA context into trajectory images and leveraging CNN-based models. It introduces a holding-featurization module that incorporates leading aircraft holding status and time/space gaps, enabling end-to-end ALT prediction. On Singapore Changi data, the approach achieves substantial accuracy gains, reducing MAE from 82.23 s to 43.96 s and achieving 96.1% average accuracy with 79.4% of errors under 60 s. The method reduces feature engineering needs while capturing spatial-temporal interactions among arrivals, offering practical benefits for ATC sequencing and airport operations, with future extensions to longer ranges and STAR/ATC integration.

Abstract

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the aircraft entering the research airspace that covers the Terminal Maneuvering Area (TMA). Specifically, the trajectories of all airborne arrival aircraft within the temporal capture window are used to generate an image with the target aircraft trajectory labeled as red and all background aircraft trajectory labeled as blue. The trajectory images contain various information, including the aircraft position, speed, heading, relative distances, and arrival traffic flows. It enables us to use state-of-the-art deep convolution neural networks for ALT modeling. We also use real-time runway usage obtained from the trajectory data and the external information such as aircraft types and weather conditions as additional inputs. Moreover, a convolution neural network (CNN) based module is designed for automatic holding-related featurizing, which takes the trajectory images, the leading aircraft holding status, and their time and speed gap at the research airspace boundary as its inputs. Its output is further fed into the final end-to-end ALT prediction. The proposed ALT prediction approach is applied to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from November 1 to November 30, 2022. Experimental results show that by integrating the holding featurization, we can reduce the mean absolute error (MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of 96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.

Figures (11)

• Figure 1: Overview of the Proposed Method.
• Figure 2: Arrival Aicraft Trajectories in One Day for Changi Airport.
• Figure 3: Aircraft Landing Time vs RECAT-EU.
• Figure 4: Runway Threshold Usage in Two Days.
• Figure 5: Trajectories with Holdings in Two Days.