Autonomous Assessment of Airspace Operations

NASA’s 2018 SBIR solicitation topic A3.02 requests “Autonomous systems to produce any of the following system capabilities: Prognostics, data mining, and data discovery to identify opportunities for improvement in airspace operations.” Identifying opportunities for improvement is a critical ongoing need in the air traffic management domain, for which achieving high levels of performance is a daily concern. In Phase I, Mosaic ATM developed and delivered a machine learning model to predict terminal area (TRACON) transit times for flights arriving to Dallas/Fort Worth International Airport (DFW) based on a broad array of flight and weather input data. We also delivered a visualization capability that enables analysts to connect observed variations in TRACON transit times with their most important causes. For Phase II, we will build upon our Phase I outcomes by expanding both the sophistication of our methodologies and the performance domains we address. We will develop deep learning models of NAS performance metrics for two challenging domains: the capacity of New York area airports and the capacity of en-route airspace in the Eastern US. We will advance the state of the art in explainable deep learning for complex systems by following a rigorous process for extracting understandable basis vectors, by iteration over the internal variables of a hybrid neural network. By doing those two things, we will achieve a foundation for traffic manager decision support enhancements, to provide guidance on how to configure the Time-Based Flow Management (TBFM) system and the Traffic Flow Management System (TFMS) to maximize system-level performance. Finally, we will implement a user interface that is integrated with the NASA Sherlock ATM data warehouse, to enable NASA domain analysts to explore the models and the important features affecting performance, in a collaborative way.

Expand deep learning models and user interface developed in Phase II within Sherlock data warehouse for use by NASA and partners on ATM-X and other research efforts Guide future research efforts aimed at improving NAS performance; advise FAA on optimizing use of TBFM and TFMS

Develop real-time tools capable of providing explainable advisories of airport and airspace capacity over a near-term time horizon for integration within an existing Mosaic ATM platform Adapt the methodology and framework to run within Mosaic ATM data warehouse to enable analyses for other government agencies and commercial NAS stakeholders

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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