  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).
