Mosaic sought to provide an open and accessible model to predict the impacts of wind on air miles flown under an SBIR contract with NASA. Ultimately, this effort has developed a model that the broader aviation community can use as a publicly available data service.
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.
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.
Mosaic ATM Case Study | 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 Read more…
The FAA needed an aviation technology partner that had substantial expertise in fusing data streams like this. The FAA turned to Mosaic ATM, a leading aviation technology company, to build this data integration capability. Mosaic developed a data exchange system to assist the FAA SWIM program in delivering the correct information, to the right users, at the right time.
As part of the ATD-2 effort, Mosaic, in conjunction with NASA, evaluated an ML-driven approach against the controller-derived assignments, hypothesizing that the performance would be equivalent to that of an expert air traffic controller.
We built a predictive machine learning model that incorporates weather forecasts and air traffic movements to provide decision support to air traffic controllers.