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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Assessment of Ramp Times 4 (ART-4) Human-in-the-Loop (HITL) Simulation - Final Results

Airspace Technology Demonstration 2 (ATD-2) sub-project conducted a Human-in-the-Loop (HITL) simulation to assess Ramp Controllers ability to deliver aircraft to the spot within the compliance window (+/- 5 min) under various metering conditions. Compliance at the spot was similar between the different metering conditions ranging between 83% - 85% and increased to 92% - 99% when aircraft were initially compliant with gate advisories. Metering benefits that exist in the field did not appear in the simulation due to simulation artifacts such as gate holding departures in Baseline, which effectively metered the demand. The combined Target Off-Block Time (TOBT) +Target Movement Area entry Time (TMAT) condition resulted in higher workload on the Workload Assessment Keypad (WAK) than the Baseline and TOBT alone conditions, and lower situation awareness than the Baseline condition. Metering at Dallas/Fort Worth International Airport (DFW) with TOBT only or TMAT only could be equally effective, and either would be a better option than TOBT + TMAT due to increased workload and reduced situation awareness.

Jung, Yoon C.↗
NASA NTRSDate not supplied

System-Wide Benefit Metrics

The presentation discusses goals for real-time metrics, new fields available in the ATD-2 System for displaying metrics to users, and analyses on delay prediction accuracy and delay savings.

Jeremy Coupe↗
NASA NTRS2020

Surface Trajectory-Based Operations (STBO) Client User Manual

This document serves as a user manual for the ATD-2 Surface Trajectory-Based Operations (STBO) Client (version 5.11) utilized by Air Traffic Control in the Tower. It describes the elements of the STBO Client interface and provides step-by-step instructions for using the tool. STBO Client functionality includes the display of live flight information, management of traffic restrictions, and prediction of expected traffic demand. The STBO Client is a component of the NASA Airspace Technology Demonstration 2 (ATD‑2) sub-project.

Louise Kay Morgan Ruszkowski↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

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.

machine learning↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

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.

machine learning↗
NASA NTRS2018

Ramp Traffic Console (RTC) Ramp Manager Traffic Console (RMTC) User Manual

This document serves as a user manual for the Ramp Traffic Console (RTC) in the Ramp Control Tower. It describes the elements of the RTC interface and provides explanations for how to utilize the RTC to manage ramp traffic. The RTC provides live data for all flights including Earliest Off-Block Times (EOBT) and Traffic Management Initiatives (TMI). The RTC augments management of ramp traffic by providing notifications of runway configurations, and lists flight arrivals, near arrivals and departures as additional sources of information. If applicable, this document also provides instructions for use of the Ramp Manager Traffic Console (RMTC) for ramp manager functions of adjusting the priority flight list, and setting ramp status. The RTC/RMTC ramp tool are components of Airspace Technology Demonstration-2.

ATD-2 RMTC↗
NASA NTRS2015

Methods of Increasing Terminal Airspace Flexibility and Control Authority

The focus of the NRA contract is to develop a What-if Analysis Tool for planning Departure Management Programs (DMP) at airports. This final report summarizes the work conducted throughout the base year, with a focus on use case specification for the what-if analysis capability and the implementation of the What-if Analysis Tool and its application to traffic and weather scenarios at Charlotte Douglas International Airport (CLT).

NRA Final Report↗
NASA NTRS2016

Development of Methods of Increasing Terminal Flexibility and Control Authority: Option Year 1 Final Report

The focus of the NRA contract is to develop a What-if Analysis Tool for planning Departure Management Programs (DMP) at airports. This final report summarizes the work conducted throughout the option year, with a focus on use case specification for the what-if analysis capability and the implementation of the What-if Analysis Tool and its application to traffic and weather scenarios at Charlotte Douglas International Airport (CLT).

Departure Management Programs (DMP)↗
NASA NTRS2019

Formulative Input into Future NASA Aeronautics Planning

This presentation covers industry input received for future work in NASA Aeronautics over the next 5 years. It is intended to present areas of significant imput and to stimulate further discussion.

future aeronautics planning↗
NASA NTRSDate not supplied

Surface Meets TOS Update & Potential Future Work

This briefing for the Surface CDM (Collaborative Decision Making) Team (SCT) & Flow Evaluation Team (FET) discussed the foundational data/information quality needs to enable a collaborative TOS concept that includes surface. The primary goal of this briefing was on soliciting broad feedback from the operator community. This discusses the importance of the Earliest Off Block Time (EOBT) as a predictor for improving NAS demand predictions as well as possible areas where SCT/FET could contribute to uses of Trajectory Option Sets in future FAA systems.

ATD-2, IADS, SCT/FET↗