Description: This CCRPP SBIR research provides a significant improvement over current-day near-real-time National Airspace System (NAS) performance metrics computation and visualization methods with significant commercialization potential. The FAA has identified lack of near-real-time NAS performance metrics as a key shortfall, which inhibits its ability to assess the current state of the NAS and to make data-driven decisions. Closely related to this need, ATAC has developed several aviation data analytics- and machine learning-based NAS performance metrics generation and visualization capabilities under the Phase I, II, and II-Extended CIDAS-P SBIR projects. However, all these SBIR capabilities work in a post-operations analysis mode, i.e., they apply to recorded historical data. Under the proposed CCRPP work, ATAC plans to infuse CIDAS-P SBIR technologies into FAA AJR-G data processing systems and move the metrics generation and visualization to a near real-time paradigm. Under the proposed work, ATAC enhances FAA’s existing real-time track data processing capabilities as required for near-real-time NAS performance metrics generation. ATAC also develops near-real-time data ingestion, cleaning, and processing capabilities for several non-track data sources. Next, ATAC applies innovative CIDAS-P machine learning capabilities to identify hidden trends, key events, and anomalies in the incoming real-time data streams to generate insightful, actionable NAS performance metrics. Finally, the developed metrics are displayed on dashboards using the FAA’s Virtual PDARS platform. This CCRPP SBIR technology infusion will provide the FAA with strategic and tactical NAS oversight and the capability to regulate real-time air traffic when constraints such as airport surface delays, airborne holding, etc. affect the NAS. It also provides key lessons learned to support NASA ATM-X project’s efforts to develop small digital services to aid domestic airlines and airports.

Benefits: ATM-X: Collective inference based real-time situational awareness and performance metrics on the Digital Information Platform (DIP); ATD-2: CIDAS-P collective inference based ops analysis complements ATD-2 CLT operations analysis; continuous monitoring of operations supports Strategic Scheduler development SWS: Reliable, automated collective inference based guidance on false positive and missed anomaly detection alerts

FAA: Integration with NAS performance data analytics and visualization for improved situational awareness; TBO benefits analysis and monitoring capability; NAS weather impact analysis tool to support TFM planning. Airlines/Airports: Improved analysis of irregular operations responses, airline network-wide flight scheduling, gate turnaround and ramp operations, diversions and cancellations.