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Initial Analysis of and Predictive Model Development for Weather Reroute Advisory Use

In response to severe weather conditions, traffic management coordinators specify reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options would assist the traffic management coordinators in making more efficient rerouting decisions. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. Essentially, using previous information to inform future decisions. This paper describes the initial steps and methodology used towards this goal. A method to extract relevant features from the large volume of weather data to quantify the convective weather scenario during a particular time range is presented. Similar routes are clustered. A description of the algorithm to identify which cluster of reroute advisories were actually followed by pilots is described. Models built for fifteen of the top twenty most frequently used reroute clusters correctly predict the use of the cluster for over 60 of the test examples. Results are preliminary but indicate that the methodology is worth pursuing with modifications based on insight gained from this analysis.

machine learning↗

Concept of Operations for Management by Trajectory

This document describes Management by Trajectory (MBT), a concept for future air traffic management (ATM) in which every flight operates in accordance with a four-dimensional trajectory (4DT) that is negotiated between the airspace user and the Federal Aviation Administration (FAA) to respect the airspace user's goals while complying with National Airspace System (NAS) constraints. In the present-day NAS, the ATM system attempts to predict the trajectory for each flight based on the approved flight plan and scheduled or controlled departure time. However, once the aircraft starts to move, controllers tactically manage the aircraft to implement traffic management restrictions, separate otherwise conflicting aircraft, and address arising NAS constraints. Tactical controller actions are not directly communicated to the automation systems or other stakeholders. Furthermore, the initial trajectory prediction does not anticipate these disruptions or how they will impact the flight. Consequently, and compounded by gaps in required data and models, trajectory predictions are less accurate than possible, which affects Traffic Flow Management (TFM) performance. A cornerstone of the MBT concept is that all air vehicles have, at all times, an assigned 4DT from their current state to their destination. These assigned trajectories consist of trajectory constraints and descriptions. Pilots and air traffic controllers, with the aid of automation, operate the aircraft to comply with the assigned trajectory, unless first negotiating a revision. Equipped aircraft have substantial responsibility for complying with the assigned trajectory without controller intervention. To maximize the operational flexibility available to the airspace user, the assigned trajectory only imposes trajectory constraints as required to achieve the ATM goals of NAS constraint compliance and aircraft separation. Trajectory descriptions are added to the assigned trajectory to ensure sufficient predictability. To further improve trajectory prediction accuracy, airspace users supplement the assigned trajectory by broadcasting intent information and updating it as necessary. Air vehicle intent is a more detailed description of the airspace user's plan for how the flight will fly the assigned trajectory. Air vehicle intent can change freely, without negotiation, as long as it remains in compliance with the assigned trajectory. Aircraft assigned trajectories, air vehicle intent, and predicted trajectories are shared, creating a common view among stakeholders. A NAS Constraint Service gathers and publishes information about all known NAS constraints, enabling airspace users to be informed participants in trajectory negotiation. Trajectory constraints in the assigned trajectory are mapped to NAS constraints to facilitate identifying which aircraft are affected when NAS constraints change. To support efficient trajectory negotiation, all aircraft provide current information about air vehicle capabilities. Assigned trajectories are constructed to satisfy all known NAS constraints, improving trajectory stability and predictability. Uncertainty and disruptions are handled by modifying the assigned trajectory as far in advance as possible. By proactively negotiating changes to the assigned trajectory, rather than relying on controller-selected tactical actions such as vectors to resolve traffic conflicts or implement miles-in-trail restrictions, MBT keeps aircraft on closed trajectories that are fully known to all stakeholders. Since reactive air traffic control actions cannot be predicted in advance, the downstream trajectory cannot be accurately predicted until they happen. Reliable trajectory predictions allow the system to identify needed modifications to trajectories further in advance, where they can be negotiated and communicated as amendments (i.e., additional or altered trajectory constraints) to the assigned trajectory. Decision Support Tools (DSTs) aid controllers in rapidly defining and communicating closed trajectories to the aircraft and support all stakeholders in trajectory negotiation. Anticipated MBT benefit mechanisms include more accurate trajectory predictions, improved ATM performance and robustness to off-nominal conditions, increased flexibility and operational efficiency, reduced impediments to emerging classes of airspace users accessing NAS resources, reduced environmental impacts, and enhanced safety.

ConOps↗

Validation of an Automated System for Arrival Traffic Management

The fuel-efficiencies of arrival flights that were managed by an automated system were compared to the fuel-efficiencies of arrival flights that were managed by air traffic controllers. It was infeasible to have the automated system control arrivals in real operations, so the comparison was accomplished by setting up a fast-time simulation where the automated system could manage arrivals with the same initial conditions and flight plans as those that operated in real operations during a selected comparison period and in the same background traffic. For this study, Newark Liberty International Airport was selected as the arrival airport because its high traffic load and constrained arrival procedures were expected to highlight fuel-efficiency benefits of an automated system. In the simulation, the automated system managed Newark arrivals, and the other flights (arrivals to other airports, departures, and overflights) composed the background traffic. To match the simulation and the real operations background traffic, the other flights flew in simulation the same trajectory that they flew in real operations during the comparison period. Fuel-efficiency was measured by calculating fuel burns of the arrival trajectories. The fuel-efficiencies of arrival trajectories produced in the simulation were compared with the estimated fuel-efficiencies of arrival trajectories recorded from real operations during the comparison period. Results showed that automation managed arrivals burned 346 lbs less fuel per flight on average than controller managed arrivals.

air traffic control↗

Validation of an Automated System for Arrival Traffic Management

The fuel-efficiencies of arrival flights that were managed by an automated system were compared to the fuel-efficiencies of arrival flights that were managed by air traffic controllers. It was infeasible to have the automated system control arrivals in real operations, so the comparison was accomplished by setting up a fast-time simulation where the automated system could manage arrivals with the same initial conditions and flight plans as those that operated in real operations during a selected comparison period and in the same background traffic. For this study, Newark Liberty International Airport was selected as the arrival airport because its high traffic load and constrained arrival procedures were expected to highlight fuel-efficiency benefits of an automated system. In the simulation, the automated system managed Newark arrivals, and the other flights (arrivals to other airports, departures, and overflights) composed the background traffic. To match the simulation and the real operations background traffic, the other flights flew in simulation the same trajectory that they flew in real operations during the comparison period. Fuel-efficiency was measured by calculating fuel burns of the arrival trajectories. The fuel-efficiencies of arrival trajectories produced in the simulation were compared with the estimated fuel-efficiencies of arrival trajectories recorded from real operations during the comparison period. Results showed that automation managed arrivals burned 346 lbs less fuel per flight on average than controller managed arrivals.

air traffic control↗

Cooperative Upper Class E Airspace: Concept of Operations and Simulation Development for Operational Feasibility Assessment

Upper Class E Traffic Management (ETM) is a novel community-based traffic management concept incorporating the FAA, NASA, and industry’s collaborative efforts to support safe, efficient, and scalable future operations in the airspace near and above 60,000 ft. The concept complements Air Traffic Control (ATC) infrastructure and Air Traffic Management (ATM) services by facilitating cooperative operations with ETM provided services. This paper presents an initial Cooperative Separation Management (CSM) concept for High Altitude Long Endurance (HALE) vehicles’ conflict detection and resolution during their extended operations. A prototype simulation platform has been developed to visualize and assess the concept during demonstrations to stakeholders. Further development plans for simulations and the CSM concept are discussed.

upper Class E airspace↗

Cooperative Upper Class E Airspace: Concept of Operations and Simulation Development for Operational Feasibility Assessment

Upper Class E Traffic Management (ETM) is a novel community-based traffic management concept incorporating the FAA, NASA, and industry’s collaborative efforts to support safe, efficient, and scalable future operations in the airspace near and above 60,000 ft. The concept complements Air Traffic Control (ATC) infrastructure and Air Traffic Management (ATM) services by facilitating cooperative operations with ETM provided services. This paper presents an initial Cooperative Separation Management (CSM) concept for High Altitude Long Endurance (HALE) vehicles’ conflict detection and resolution during their extended operations. A prototype simulation platform has been developed to visualize and assess the concept during demonstrations to stakeholders. Further development plans for simulations and the CSM concept are discussed.

upper Class E airspace↗

The Knowledge-based Digital Platform Concept for Advanced Air Mobility Research and Development

National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.

ATM↗

Initial Concept of Operations for Full Management by Trajectory

This document describes Management by Trajectory (MBT), a concept for future air traffic management (ATM) in which flights are assigned four-dimensional trajectories (4DTs) through a negotiation process between the Federal Aviation Administration (FAA) and flight operators that respects the flight operator's goals while complying with National Airspace System (NAS) constraints.

Fernandes, Alicia D.↗

Federal USS Synthesis Effort: 2023 Flight Test Report. A NASA and Department of Defense Joint Evaluation of Multi-Domain Airspace Integration

In recent years, advancements in technology have greatly improved the utility and applicability of Uncrewed Aircraft Systems (UAS) and have highlighted the need for and importance of UAS Traffic Management (UTM). Onboard sense and avoid systems, ground-based sensors, and long- range command and control (C2) systems have significantly expanded the possibilities for UAS beyond visual line of sight (BVLOS) operations. Initially, research on UTM flights focused on their interactions with other UTM operations, but that has since been expanded to include interactions with the flight profiles of Advanced Air Mobility (AAM) and upper Class E Traffic Management (ETM) operations. Recognizing the potential of these systems and the increase in UAS autonomy, the Department of Defense (DoD) is working towards integrating these missions and systems into a unified DoD Federal ecosystem. The Federal USS Synthesis Effort (FUSE) aims to develop a live system that can efficiently combine this wide range of UTM, AAM, and ETM systems, thus enabling expanded operations within a single common operating environment. The goal of the Federal USS Synthesis Effort (FUSE) is for NASA and the DoD to develop a live system that can efficiently combine UTM, AAM, and ETM systems. The most recent endeavor in this effort was a live flight test conducted near Grand Forks Air Force Base in June, 2023, where the FUSE ecosystem successfully demonstrated integrating UTM, AAM, and ETM operations while providing a single, shared COP to users. Additional investigations were made during the test, including assessing the usefulness of the system and its capability to support UAS weighing over 55 lbs.; testing user messaging capabilities and a DoD adaptation of a Federal USS; and assessing user workload, automated vs manual control, and the usability of NASA’s XTMClient.

Federal USS↗

Initial Approach to Collect Small Unmanned Aircraft System Off-Nominal Operational Situations Data

NASA is developing the Unmanned Aircraft System Traffic Management research platform to safely integrate small unmanned aircraft operations in large-scale at low-altitudes. As a part of this effort, small unmanned aircraft system off-nominal operational situations data collection process has been developed to take lessons learned and to reinforce operational compliance. In this paper, descriptions of variables used for digital data collection and an online report form for collection of observational data from the operators (contextual data) are provided. They are used to collect off-nominal data from the Unmanned Aircraft System Traffic Management National Campaign in 2017. The digital data show that 2 out of 118 campaign operations (1.7%) encountered loss of navigation. Since the campaign aircraft used Global Positioning System for navigation, it is likely that unobstructed view of the sky at the campaign locations contributed to this small number. Also, 4 out of 47 operations (8.5%) encountered loss of communications. A relatively short distance between ground control system and aircraft, ranging from 2300 feet to 4200 feet, likely contributed to this small number. There was no data to identify the loss of communications condition, aircraft received signal strength, for the remaining 71 operations suggesting that some operators may not be monitoring unmanned aircraft communications system performance or monitoring it with different parameters. For the contextual data, due to the low number of total reports during the campaign, no significant trends emerged. This is an initial attempt to collect contextual data from small unmanned aircraft operators about off-nominal situations, and changes will be made to the future data collection to improve the amount and quality of the information.

unmanned aviation systems traffic management (UTM)↗

ATD-3 Dynamic Routes for Arrivals in Weather (DRAW) Operational Concept, V2.0

NASA's Airspace Technology Demonstration-3 (ATD-3) is the applied traffic flow management activity, and third in the series of ATD projects. ATD-3 provides a suite of en route automation tools, both ground and flight-deck based, that focus on improving the efficiency of en route operation from initial cruise to arrival into the Terminal Radar Approach Control (TRACON). Dynamic Routes for Arrivals in Weather (DRAW) is a technology in the ATD-3 suite that is designed to provide Traffic Managers with a capability to efficiently manage arrival traffic flow and help sustain metering operations when weather is impacting arrivals into major airports. DRAW mitigates convective weather impact on arrival metering operations by providing tools to Traffic Managers that enable efficient reroutes free of convective weather conflicts, and integrated with the arrival metering schedule(s).

terminal airspace↗

Initial Investigations of Controller Tools and Procedures for Schedule-Based Arrival Operations with Mixed Flight-Deck Interval Management Equipage

NASA's Air Traffic Management Demonstration-1 (ATD-1) is a multi-year effort to demonstrate high-throughput, fuel-efficient arrivals at a major U.S. airport using NASA-developed scheduling automation, controller decision-support tools, and ADS-B-enabled Flight-Deck Interval Management (FIM) avionics. First-year accomplishments include the development of a concept of operations for managing scheduled arrivals flying Optimized Profile Descents with equipped aircraft conducting FIM operations, and the integration of laboratory prototypes of the core ATD-1 technologies. Following each integration phase, a human-in-the-loop simulation was conducted to evaluate and refine controller tools, procedures, and clearance phraseology. From a ground-side perspective, the results indicate the concept is viable and the operations are safe and acceptable. Additional training is required for smooth operations that yield notable benefits, particularly in the areas of FIM operations and clearance phraseology.

Callantine, Todd J.↗

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

Corker, Kevin M.↗

ATM-X and Urban Air Mobility Overview

These slides are material for the Initial Urban Air Mobility (UAM) sub-project to provide an overview of Air Traffic Management eXploration (ATM-X) project and UAM to external collaborators.

Air Traffic Management –eXploration↗

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

data management↗

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

air traffic management↗

Initial Analysis of and Predictive Model Development for Weather Reroute Advisory Use

In response to severe weather conditions, traffic management coordinators (TMCs) reroute air traffic around regions of airspace affected by the severe weather. Presently, acceptable reroutes are specified by issuing reroute advisories. Advisories, based on reroutes published in the National Severe Weather Playbook, consist of several routes describing routing options. These reroutes are selected by TMCs based on their understanding of weather conditions and their previous experience dealing with similar weather conditions. Providing recommendations and analysis of available reroute options could assist the TMCs in making rerouting decisions. While reroute advisories have historically been based on Playbook plays, plays are frequently modified or combined to generate an advisory. Thus, it is natural to present rerouting suggestions to TMCs as modified Playbook plays. The challenge here is to compare advisory subroutes and play subroutes in a meaningful way in order to map advisories back to the Playbook plays upon which they may have been based. In this work, several subroute comparison metrics are presented and discussed. One of these metrics is then used to match advisories to Playbook plays. With the development and implementation of NextGen technologies, there is currently a trend of moving away from the pre-defined Playbook plays (and related advisories) in favor of a more precise specification of trajectories using GPS based navigation tools, such as area navigation (RNAV). Moving towards ubiquitous use of RNAV routes, the objective of rerouting flights will be met with more precisely specified and accurately flown RNAV routes. In the final version of this paper, significant RNAV routes will be identified by finding RNAV routes that are flown frequently. This will require a trajectory comparison technique to compare actually flown flight tracks to specified RNAV routes. The metrics developed here will be used or modified for this task.

weather required reroutes↗

Validating an Air Traffic Management Concept of Operation Using Statistical Modeling

Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis

Statistical emulation↗