  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.

Machine Learning