English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Aircraft landing time (ALT) prediction is crucial for air traffic management,especially for arrival aircraft sequencing on the runway. In this study, atrajectory image-based deep learning method is proposed to predict ALTs for theaircraft entering the research airspace that covers the Terminal ManeuveringArea (TMA). Specifically, the trajectories of all airborne arrival aircraftwithin the temporal capture window are used to generate an image with thetarget aircraft trajectory labeled as red and all background aircrafttrajectory labeled as blue. The trajectory images contain various information,including the aircraft position, speed, heading, relative distances, andarrival traffic flows. It enables us to use state-of-the-art deep convolutionneural networks for ALT modeling. We also use real-time runway usage obtainedfrom the trajectory data and the external information such as aircraft typesand weather conditions as additional inputs. Moreover, a convolution neuralnetwork (CNN) based module is designed for automatic holding-relatedfeaturizing, which takes the trajectory images, the leading aircraft holdingstatus, and their time and speed gap at the research airspace boundary as itsinputs. Its output is further fed into the final end-to-end ALT prediction. Theproposed ALT prediction approach is applied to Singapore Changi Airport (ICAOCode: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B)data from November 1 to November 30, 2022. Experimental results show that byintegrating the holding featurization, we can reduce the mean absolute error(MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.

Aircraft Landing Time Prediction with Deep Learning on Trajectory Images