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HIGHLIGHTS ARCHIVE
ATM-X and Uber Working to Extend Research Collaboration
March 4, 2020

The Air Traffic Management Exploration (ATM-X) project signed a multi-year Space Act Agreement (SAA) with Uber Inc. in 2018 to conduct collaborative research studies on integrating Urban Air Mobility (UAM) operations with existing air traffic operations in both controlled and uncontrolled airspace. The efforts under this annex include the “X1” part-task experiment studies to investigate methods of managing high-density UAM operations in today’s airspace, and the “X2” engineering evaluations to extend the ATM paradigm and system reference implementation developed for unmanned aircraft to UAM operations, including for manned operations.

Lessons learned and operational insights have been gained through the NASA-Uber collaboration and are documented for the benefit of the UAM community at large. NASA and Uber are pursuing a follow-on annex under the same SAA to continue these research collaborations that will accelerate airspace access and integration of UAM into the National Airspace System.

POC: Savvy Verma



Machine Learning as a Service, briefing to SWIM Industry-FAA Team (SWIFT) Forum
March 4, 2020


Al Capps presents to the System Wide Information Management (SWIM) Industry-Federal Aviation Administration (FAA) Team at the FedEx Institute of Technology.

On February 26, 2020, NASA briefed the System Wide Information Management (SWIM) Industry-Federal Aviation Administration (FAA) Team (SWIFT) in Memphis, TN at the FedEx Institute of Technology on the University of Memphis campus. Approximately 200 participants from all the major airline operator groups and aviation solution providers (see member list image below) participated in the meeting.

NASA’s presentation included two specific examples of machine learning solutions developed by applying a data mining problem solving workflow to aviation challenges. The workflow NASA used is loosely based upon the cross industry standard process for data mining (CRISP-DM) which has been used successfully in other industries. The first example demonstrated a gradient boost classifier that improved arrival runway prediction classification by approximately 20% over the available sources in SWIM today. NASA described the steps performed to deploy this machine learning model to a web service using commonly available open source technology.

The second example demonstrated a gradient boost regressor that estimates the size of tactical delay impact into a busy airspace resource in the Northeast Corridor. This example responds to a top need identified by the SWIFT analytics team at their first meeting in January. The data and visualization from this learner generated significant feedback from the meeting participants.

NASA also highlighted the importance of frequent engagement between analysts and operational users in the SWIFT community and the need to describe aviation operations challenges into data science formats so that the aviation industry can benefit from this significant new technology.



List of SWIFT stakeholders as briefed in FAA SWIFT Forum on February 26, 2020

POC: Al Capps



ATD-2 Training at Atlanta and Washington Center
March 4, 2020


Bob Staudenmeier and Mark Ellerbee (Atlanta Center Traffic Management Unit)

On February 25-26, 2020, the NASA Airspace Technology Demonstration 2 (ATD-2) team visited the Atlanta Air Route Traffic Control Center (ZTL ARTCC) in Hampton, GA to train new traffic management coordinators (TMCs) and Supervisory TMCs (STMCs) on the ATD-2 system. Additionally, refresher training was provided to all TMCs on new features that have been incorporated into the system over the last several software releases. All TMCs were briefed on both the capabilities of ATD-2’s Ramp Traffic Console (RTC) and the Surface Trajectory Based Operations (STBO) Client. During each session, TMCs were guided through interactions with the RTC and STBO Client interfaces, were introduced to features and data exchange between the two tools, and discussed how ATD-2 is integrated into their Time Based Flow Management (TBFM) system through Integrated Departure Arrival Capability (IDAC) and the non-verbal interaction that is available through electronic negotiations. TMCs had the opportunity to provide feedback on potential use in the ZTL environment. TMCs were also introduced to the concept of ‘pre-scheduling’ and discussed how well that has been working with flights from Atlanta and Chicago. Outcomes of the training, beyond proficiency on the ATD-2 systems, included increased use of the tool especially during severe weather conditions.

POC: Pete Slattery, Bob Staudenmeier



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