As a Software Engineer 2 on FlightAwareâs Predictive Technology crew, Andrew Brooks continually works to maintain, analyze, and improve FlightAwareâs predictive models as well as the software and infrastructure that supports them.
Foresight is the name weâve given to FlightAwareâs suite of predictive technologies, which use machine learning models to emit real-time predictions about flights. These models draw upon datasets that combine thousands of data sources and incorporate routing and weather data to forecast future events in real-time.
In this blog post, weâre going to focus on Foresightâs real-time ETA predictions. Specifically, Foresight can provide two kinds of ETAs: an estimated âonâ time (when a flight lands on its destinationâs runway) and an estimated âinâ time (when a flight has finished taxiing to its gate at the destination). Foresight ETA predictions require two machine learning models for each destination, which provide the âonâ and âinâ predictions, respectively.
Training the machine learning models that power Foresight ETAs is not an easy task. In order to support thousands of destinations around the world, we need to train about 3500 different models. To make matters worse, these models must be retrained once per month, from scratch, to ensure that theyâre able to adapt to any changes in real world conditions.