Run-way Functions: Predict Reconfigurations at US Airports (Open Arena) Hosted By NASA


About the project

The nature of air travel is connecting far-flung places; the downside is that events in one location can have ripple effects throughout the system. Effective decision-making needs to consider a large number of factors, and the right decision might not always be the intuitive one. Furthermore, the drivers for these decisions are highly uncertain systems in their own right;  weather is the major driver of delays in the NAS, for example. Airlines and the Federal Aviation Administration have many decision-support tools at their disposal to make the most informed decisions as possible. The stakeholders for these tools are always seeking better algorithms, predictors, optimization approaches, and other elements to gain some predictability and save on costs.

Prior work in airport configuration prediction has relied upon custom solutions for individual airports, based on case studies, rigorous analysis of historic data, and interviews with airport operators and air traffic controllers to define the process used to make configuration decisions. This method requires a large amount of manual effort for each airport at which the system should work. Two factors are converging to make this process easier and more accurate in the future. The first is simply the ongoing advancement in machine learning tools and processes. The second is the development of bigger and better real-time streams for flight, weather, and other types of data that can feed the machine learning methods.

The FAA works with airports, airlines, and other flight operators to collect raw data about all flights in the NAS and distribute that data via SWIM (System-Wide Information Management). As part of the Airspace Technology Demonstrations 2 (ATD-2) project, NASA developed Fuser to process this torrent of raw data and provide cleaned, real-time data on the status of individual flights nationwide, facilitating downstream air traffic management tools. The ATD-2 project has already used Fuser to implement various data-driven machine learning systems to predict airport configuration, runway assignment, taxi time, and more.

About the NASA team

In recent years, the amount of data available in the NAS has exploded, as has the capability of data science algorithms to extract meaning and make decisions from large volumes of data. For example, NASA’s Airspace Technology Demonstrations project has made use of machine learning as part of the Integrated Arrival/Departure/Surface (IADS) system to optimize traffic at airports. Efforts such as this have shown that the main bottleneck now is the effort of accessing, understanding, and consuming many disparate data feeds from many different sources.

The goal of the Digital Information Platform is to provide a consistent, easy to use platform where a wide variety of NAS data is readily available. This will accelerate the transformation of the NAS by facilitating the development of state-of-the-art data-driven services for use by both traditional airlines and emergent operations like Unmanned Aerial Systems and Urban Air Mobility.

This challenge will function as an early use case to demonstrate the potential that such services can reach with access to high-quality data. Not only will the winning algorithms inform future work on airport configuration prediction, but every team’s experience during the challenge will inform the design and development of the Platform as a whole.

Additional resources