Abstract: A method is provided for predicting a reroute for a planned flight of an aircraft. The method includes building a machine learning model to predict a reroute on a future date of a planned flight of an aircraft. The machine learning model is built in a batch process that includes accessing reroute data and weather data, and performing a data wrangling of the reroute data and the weather data to produce a collection of data keyed by date. Candidate machine learning models are built using a training set produced from the collection of data. The candidate machine learning models are evaluated, and the machine learning model is selected from the candidate machine learning models based on the evaluation. And the machine learning model is output for deployment to classify the future date as having a reroute advisory issued, and predict a reroute on the future date.

Abstract: Examples provide a method and apparatus for predicting airport utilization. A plurality of data sources provides raw flight data and weather data which is stored in a key value data store. The incoming raw data is cleaned, correlated and indexed. The processed data is stored in an interim data store. A plurality of competing predictive models use the processed data and historical data to generate predicted arrivals and departures for a selected runway during a selected time-period. The predicted data is compared to actual arrival and departures for the selected runway. The ML model generating predicted results most closely matching the actual arrival and departure data is selected. The selected ML model generates predicted future arrivals and future departures data for the runway based on real-time flight data and weather data for output via a user interface device.