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Pushback to the Future: Predict Pushback Time at US Airports

Airplane on a runway at airport

Coordinating our nation’s airways is the role of the National Airspace System (NAS). The NAS is arguably the most complex transportation system in the world. Operational changes can save or cost airlines, taxpayers, consumers, and the economy at large thousands to millions of dollars on a regular basis. It is critical that decisions to change procedures are done with as much lead time and certainty as possible. The NAS is investing in new ways to bring vast amounts of data together with state-of-the-art machine learning to improve air travel for everyone. In order to optimize commercial aircraft flights, air traffic management systems need to be able to predict as many details about a flight as possible. One significant source of uncertainty comes right at the beginning of a flight: the pushback time. A more accurate pushback time can lead to better predictability of take off time from the runway. Predicting pushback time depends upon factors like passenger loading, cargo loading, weather, aircraft type, and operator procedures. While available data can be used to improve these predictions, the combination of public and private sources can make it difficult to get access to all of the information needed to make the best predictions. Federated learning (FL) offers immense promise here as an approach to training central ML models using private data held by separate organizations. In Phase 1 (the current phase), your task is to train a machine learning model to automatically predict pushback time from public air traffic and weather data. Better algorithms for predicting pushback time can help air traffic management systems more efficiently use the limited capacity of airports, runways and the National Airspace System. To be eligible for prizes, finalists from Phase 1 will have the opportunity to participate in Phase 2, where they will work with NASA to train a federated version of their model.

Award: $50,000 in total prizes

Open Date: February 1, 2023

Close Date: April 17, 2023

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Last Updated
Jul 17, 2023
Sarah Douglas

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