Abstract: The modern day National Airspace System (NAS) is powered by System Wide Information Management (SWIM) which is a real-time digital data sharing infrastructure that provides a high fidelity view of the lifecycle of a flight. The newly available data within the SWIM feeds can be leveraged to help drive efficiencies in the NAS. In this talk, we investigate the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We begin with a high level description of NASA's Airspace Technology Demonstration 2 which is built upon the real-time SWIM feeds and produces the data used in our investigation. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach. We quantify our predictive accuracy and identify paths for improvement. Through this iterative process we hope to evolve our models and methods in the development of a near real-time prediction service.
The NASA Berkeley Aviation Data Science Seminar Series was launched in spring 2020 and is held weekly on Wednesdays in Stanley 106, at 11:00 AM - 12:00 PM, from January 22 through May 6. Presenters include experts in government, industry, and academia, who focus on how big data collection and machine learning are transforming aircraft, airspace, and airport operations, with topics ranging from feedback control, IoT, and IoV to autonomy, AI, and data security. All seminars are livecast and interactive across both campuses. The series is also being offered as a 1-credit course: the Berkeley course numbers are CEE198/CEE298 (class #: 33393) and CP298 (class #: 13328). This seminar series is hosted by NASA and UC Berkeley, sponsored by the Universities Space Research Association (USRA) and NASA Academic Mission Services; and presented by UC Berkeley's Urban Air Mobility Research Center (UAM@Berkeley), the Berkeley Institute of Transportation Studies, and BIDS.
Jeremy Coupe is an Aerospace Engineer at NASA Ames Research Center and the analytics lead for NASA's Airspace Technology Demonstration 2. He received his BS degree in Mathematics from the University of San Francisco and both MS degree in Applied Mathematics and Statistics and Ph.D. degree in Computer Engineering from the University of California, Santa Cruz, where he was a member of the Robotics and Control Lab.