This paper proposes a novel approach to predict and determine whether the average taxi- out time at an airport will exceed a pre-defined threshold within the next hour of operations. Prior work in this domain has focused exclusively on predicting taxi-out times on a flight-by-flight basis, which requires significant efforts and data on modeling taxiing activities from gates to runways. Learning directly from surface radar information with minimal processing, a computer vision-based model is proposed that incorporates airport surface data in such a way that adaptation-specific information (e.g., runway configuration, the state of aircraft in the taxiing process) is inferred implicitly and automatically by Artificial Intelligence (AI).
@article{arxiv.2111.09139,
title = {Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach},
author = {Erik Vargo and Alex Tien and Arian Jafari},
journal= {arXiv preprint arXiv:2111.09139},
year = {2021}
}