Case Studies

ATD-2 Machine Learning Model Validation Framework Design

To realize the full power of machine learning, organizations need to focus on operations and delivery of the predictions as much as the development of the algorithm itself. In the case of NASA and ATD-2, building trust in the machine learning predictions is essential to expanding their use to improve inefficient operations and unused capacity. Air traffic controllers must trust the recommendations presented by them, and validation is essential towards building trust.

By Selina Gerosa, ago

Taxi Time Predictions

The taxi-out time predictions help pilots decide when to “single-engine taxi”: taxi most of the way to the runway with only a single-engine turned on, turning on the second engine just a few minutes before take-off. The single-engine taxi decision is typically made by pilots within an hour of push back. Still, our customer asked for predictions up to four hours in advance of the expected push back time.

By Selina Gerosa, ago

TBFM in the Cloud

Use of the Cloud has allowed commercial and Government systems to leverage the flexibility, scalability, reliability, and security of Cloud infrastructure to achieve significant efficiencies, and rapid innovation, in complex operational environments. The associated move to a micro-services Service Oriented Architecture (SOA) in the Cloud has further enhanced modularity, re-use of system capabilities, and simplification of verification and validation testing.

By Selina Gerosa, ago

ATD-2

Addressing Inefficiency in Today’s Air Transportation System According to NASA1, many of today’s air transportation system issues can be attributed to a lack of information sharing amongst the operators responsible for managing air traffic in busy terminal environments. Concepts and technologies to improve the arrival, departure, and airport surface traffic Read more…

By Selina Gerosa, ago