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High-level overview of capability and evaluation of a NASA ATD-2 prototype system that enables Flight Operators and Air Traffic Management to manage reroute of departures in Dallas, TX.
Overview of developments and results of a NASA ATD-2 prototype system that enables Flight Operators and Air Traffic Management to manage reroute of departures in Dallas, TX.
Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.
Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.
This document describes the Departure Runway Model (DRM) Machine Learning (ML) service developed under the ATD-2 subproject as a reference implementation and released by NASA as Open Source software on github.
NASA’s Digital Information Platform is creating a digital information ecosystem to exchange services and provide access to airspace information to enable fuel efficient operations. This system allows providers to make their services more accessible and consumers to access information and services they need to optimize their operations. In this video we will focus on the Collaborative Digital Departure Rerouting service, which provides airlines with routing options similar to how drivers navigate using cellphone apps.
Collaborative Digital Departure Reroute (CDDR) enables the reroute of flights using a flight operator proposed set of alternative route options, referred to as Trajectory Option Set (TOS), in order to reduce delay on the airport's surface and in the Metroplex environment. The reroute functionality is enabled through NASA's Digital Information Platform (DIP). TOS candidate flights are defined as flights with an alternative route with delay savings greater than the flight operator defined relative trajectory cost. This paper analyzes the TOS candidate flights at Dallas/Fort Worth International Airport (KDFW) in the North Texas Metroplex to gain insight into which candidate flights are higher quality through a scoring method. This insight will inform refinements to help CDDR focus on high quality reroute opportunities. Binary classification models for predicting the flight operator's submission of candidate flights are also explored in this paper.
Collaborative Digital Departure Reroute (CDDR) enables the reroute of flights using a flight operator proposed set of alternative route options, referred to as Trajectory Option Set (TOS), in order to reduce delay on the airport's surface and in the Metroplex environment. The reroute functionality is enabled through NASA's Digital Information Platform (DIP). TOS candidate flights are defined as flights with an alternative route with delay savings greater than the flight operator defined relative trajectory cost. This paper analyzes the TOS candidate flights at Dallas/Fort Worth International Airport (KDFW) in the North Texas Metroplex to gain insight into which candidate flights are higher quality through a scoring method. This insight will inform refinements to help CDDR focus on high quality reroute opportunities. Binary classification models for predicting the flight operator's submission of candidate flights are also explored in this paper.
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Mariner IV spacecraft details noting mission profile and requirements, design goals and use of redundancy for reliability increase
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Cosmological-galactic creation, steady state expansion and high energy particle production associated with radio sources
Jeans escape rate error for atmospheric H and He attributed to perturbed velocity distribution function, using realistic atom-atom elastic scattering cross sections
Dynamics and stability of obtaining and interpreting bubble contact angles and detachment diameters
The detection of tides and/or currents through the analysis of data generated in connection with the Ocean Geoid Determination Investigation is presented. A discussion of the detailed objectives and approach are included.
Photoelectric scans of the Ca I line at 6572 A and the forbidden Ca II transition at 7323 A are studied in the K giant alpha Tau, the M supergiant alpha Ori, and the M giants beta And, alpha Cet, mu Gem, and beta Peg. The relative strengths of these lines are shown to be indicative of the ratio of the relative number densities of the neutral and ionized species in the photosphere. The analysis indicates an overionization relative to LTE in qualitative agreement with the theoretical calculations of Auman and Woodrow for the K and M giants. The M supergiant alpha Ori exhibits a large overionization relative to LTE.
A study of forbidden S II red doublet spectra indicates that the Io plasma torus at 5.9 Jupiter radii does not corotate rigidly with Jupiter. The lag is found to be 6 percent + or 4 percent, where the variability range, not the uncertainty, is indicated. Comparison with existing models indicates the lag may be due primarily to ion creation in the Io torus.
The deposit of nonradiative heat and momentum in the mantle of a hot star affects the interpretation of the stellar spectrum in two ways. First, a superheated and moving plasma should be considered when doing the analysis, and second, a model atom which is appropriate for the physical state of the line forming regions. Some examples are presented for H and He showing how the changes in the electron temperature affect the solution of the equations of statistical equilibrium. The observed spectra of the Wolf-Raynet stars HD 191765, HD 192103, and HD 192163 are compatible with a normal H/He abundance ratio.