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Small Business Innovation Research/Small Business Tech Transfer

Collective Inference Based Data Analytics System for Post Operations Analysis Phase II

Completed Technology Project
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Project Description

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This SBIR research provides a significant improvement over current-day post operations analysis (POA) with significant commercialization potential. In Phase I, ATAC developed a machine learning based aviation POA decision support tool (DST), which improves the state of the art of today’s airline, airport and FAA POA processes by providing automated, results-oriented POA outcomes. We provided a proof-of-concept for this POA DST by demonstrating how the Phase I prototype allows airline, airport and FAA personnel at the Charlotte Douglas International Airport (CLT) to perform faster, more efficient and results-oriented post analysis of individual departure banks to obtain actionable operational insights. Encouraged by our promising proof-of-concept demonstrations in Phase I, we propose to carry forward this research in Phase II of our SBIR project towards the eventual goal of developing a commercial licensable Cloud-based POA Platform that can be accessed by NASA, FAA, airline, airport or other commercial systems or personnel in a “Platform-As-A-Service” (PAAS) mode. This proposed capability provides a one-stop platform for gate-to-gate, complete POA including aviation data acquisition, storage, analytics, and root cause diagnosis, in a post-analysis mode as well as a real-time, continuous operations monitoring mode. The proposed continuous operations monitoring mode accelerates operations analysis work related to NASA’s ATD-2 project. The proposed second airspace focused use case supports multiple NASA research programs, including ATD-2's CLT to Northeast corridor (NEC) departure flow operations analysis, IDM NEC enroute constraints analysis, ATD-3 weather-efficient routing analysis and System Wide Safety anomaly detection. Moreover, by providing the ability to perform results-oriented POA on diverse operations (UAM, IDO), the SBIR enables the future NAS to rapidly learn from operational inefficiencies, and improve new traffic management and operations paradigms. More »

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