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William J Coupe

Publications and source records attributed to William J Coupe.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

ATD-2 Field Evaluation of Pre-Departure Trajectory Option Set Reroutes in North Texas Metroplex

The NASA Airspace Technology Demonstration-2 Phase 3 capabilities extend Integrated Arrival, Departure and Surface scheduling to a Metroplex environment where multiple airports are interacting and sharing resources along the terminal boundary. The Phase 3 coordinated scheduling provides pre-departure reroute recommendations to flight operators which reduce delay caused by terminal restrictions. This paper reports results of the Phase 3 Stormy 2021 Field Evaluation conducted between November 2020 and September 2021 in the North Texas Metroplex. During the field evaluation NASA partnered with the FAA, American Airlines, Southwest Airlines, and Envoy Airlines to evaluate Phase 3 capabilities in an operational environment. The benefits results are provided as delay savings metrics measured in time and converted to fuel and emissions savings using detailed fuel flow models provided by flight operators.

ATD-2 Field Evaluation, Trajectory Option Set, Pre↗
NASA NTRS2021

Airspace Technology Demonstration 2 (ATD-2): Integrated Arrival/ Departure/Surface (IADS) System Demonstration - Technology Transfer Document Summary (ReadMe)Version 3 (Final Phase 3 Metroplex IADS)

Airspace Technology Demonstration – 2 (ATD-2) is part of NASA’s Airspace Technology Demonstrations (ATD) Project under its Airspace Operations and Safety Program (AOSP). ATD-2 is a multi-year research and development effort to improve the predictability and operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure, and surface prediction, scheduling, and management systems. In order to ensure that the products of this knowledge and technology transfer are relevant and useful, NASA has created strong partnerships with the FAA and industry stakeholders. This summary document and accompanying artifacts satisfy the final delivery of research transition products (RTPs) defined in the IADS research transition team (RTT) plan.

ATD-2↗
NASA NTRS2021

Analysis of Impacts of Terminal Restrictions on Departures in D10 TRACON

The D10 – Dallas-Fort Worth Terminal Radar Approach CONtrol (TRACON) is an air traffic facility that controls and manages aircraft and airspace that consists of multiple airports located in the North Texas area. From an air traffic management standpoint, one of the main challenges in the D10 TRACON is the departure fix capacity as multiple airports compete for resources. This problem is magnified when en route demand/capacity imbalance and inclement weather around the TRACON reduce the capacity at the terminal fixes. This leads to multiple, dynamic Traffic Management Initiative (TMI) restrictions being issued by the Air Traffic Control (ATC) on departing flights. This, in turn, propagates delay to the surface of each airport within the metroplex. The NASA Airspace Technology Demonstration 2 (ATD-2) Phase 3 is deployed in D10 to demonstrate new technologies developed to manage the Integrated Arrival, Departure, and Surface (IADS) traffic in a metroplex environment where multiple airports are interacting and sharing resources at the terminal boundary. This paper uses the ATD-2 terminal restriction data collected in the D10 TRACON to quantify the impact of restrictions on the demand, analyze the relation between terminal restrictions and departure taxi time on airport surface, and establish relationships between restrictions and surface delay. We found that the restrictions on departure flights have a direct adverse effect on departure excess taxi time on the airport surface.

ATD-2↗
NASA NTRS2021

Analysis of Impacts of Terminal Restrictions on Departures in D10 TRACON

The D10 – Dallas-Fort Worth Terminal Radar Approach CONtrol (TRACON) is an air traffic facility that controls and manages aircraft and airspace that consists of multiple airports located in the North Texas area. From an air traffic management standpoint, one of the main challenges in the D10 TRACON is the departure fix capacity as multiple airports compete for resources. This problem is magnified when en route demand/capacity imbalance and inclement weather around the TRACON reduce the capacity at the terminal fixes. This leads to multiple, dynamic Traffic Management Initiative (TMI) restrictions being issued by the Air Traffic Control (ATC) on departing flights. This, in turn, propagates delay to the surface of each airport within the metroplex. The NASA Airspace Technology Demonstration 2 (ATD-2) Phase 3 is deployed in D10 to demonstrate new technologies developed to manage the Integrated Arrival, Departure, and Surface (IADS) traffic in a metroplex environment where multiple airports are interacting and sharing resources at the terminal boundary. This paper uses the ATD-2 terminal restriction data collected in the D10 TRACON to quantify the impact of restrictions on the demand, analyze the relation between terminal restrictions and departure taxi time on airport surface, and establish relationships between restrictions and surface delay. We found that the restrictions on departure flights have a direct adverse effect on departure excess taxi time on the airport surface.

ATD-2↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This paper describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

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 NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

Machine learning↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This presentation describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

Machine Learning↗
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS2021

A Machine Learning Approach to Predict Aircraft Landing Times using Mediated Predictions from Existing Systems

We developed a novel approach for predicting the landing time of airborne flights in real-time operations. The first step predicts a landing time by using mediation rules to select from among physics-based predictions (relying on the expected flight trajectory) already available in real time in the Federal Aviation Administration System Wide Information Management system data feeds. The second step uses a machine learning model built upon the mediated predictions. The model is trained to predict the error in the mediated prediction, using features describing the current state of an airborne flight. These features are calculated in real time from a relatively small number of data elements that are readily available for airborne flights. Initial results based on five months of data at six large airports demonstrate that incorporating a machine learning model on top of the mediated physics-based prediction can lead to substantial additional improvements in prediction quality.

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 NTRS2020

ATD-2 Phase 3 Scheduling in a Metroplex Environment Incorporating Trajectory Option Sets

The NASA Airspace Technology Demonstration 2 Phase 1 and 2 Field Evaluations have successfully demonstrated new technologies developed to manage the Integrated Arrival, Departure, and Surface traffic flows at a single airport. The Phase 3 Field Evaluation extends the capabilities to a Metroplex environment where multiple airports are interacting and sharing resources along the terminal boundary. This paper describes the scheduling algorithm enabling the coordinated scheduling and describes the interaction between airports within the Metroplex and the terminal boundary. We describe the metrics developed to inform flight operators about reroute opportunities and discuss the potential benefits to the rerouted flight and the system-wide aggregate benefits of a single reroute. We believe that the capabilities developed and the lessons learned during the Phase 3 Field Evaluation will set up the National Airspace System for future success.

Airspace Technology Demonstration 2↗