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NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. 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. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗
NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. 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. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗
NASA NTRS2007

The Effects of Projected Future Demand Including Very Light Jet Air-Taxi Operations on U.S. National Airspace System Delays as a Function of Next Generation Air Transportation System Airspace Capacity

This paper presents the results from a study which investigates the potential effects of the growth in air traffic demand including projected Very Light Jet (VLJ) air-taxi operations adding to delays experienced by commercial passenger air transportation in the year 2025. The geographic region studied is the contiguous United States (U.S.) of America, although international air traffic to and from the U.S. is included. The main focus of this paper is to determine how much air traffic growth, including VLJ air-taxi operations will add to enroute airspace congestion and determine what additional airspace capacity will be needed to accommodate the expected demand. Terminal airspace is not modeled and increased airport capacity is assumed.

Smith, Jerry↗
NASA NTRSDate not supplied

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya N Das↗
NASA NTRS2022

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya Das↗
NASA NTRS2017

Airspace Technology Demonstration 2 (ATD-2) Integrated Surface and Airspace Simulation - Experiment Plan

This presentation describes the overview of the ATD-2 project and the integrated simulation of surface and airspace to evaluate the procedures of IADS system and evaluate surface metering capabilities via a high-fidelity human-in-the-loop simulation. Two HITL facilities, Future Flight Central (FFC) and Airspace Operations Laboratory (AOL), are integrated for simulating surface operations of the Charlotte-Douglas International Airport (CLT) and airspace in CLT TRACON and Washington Center.

ATD-2↗
NASA NTRSDate not supplied

Fast Aircraft Separation Calculations for Gradient Based Optimization of Airspace Simulations

Simulations of airspace operational concepts can play a significant role in determining future paradigms that would allow for a safe increase in airspace density. In particular, airspace simulations which are capable of handling large numbers of aircraft act as an enabling capability for the testing of proposed airspace operational concepts. Simulations allowing for gradient based optimization methods are particularly attractive, since they would potentially allow for an efficient and empirical means to derive best operational practices. These could also allow for vehicle multidisciplinary design and optimization studies to include air traffic management considerations as a discipline. But any large scale simulation of airspace operations must include some methodology for addressing airspace separation requirements, which in the most direct sense would be tracked in a manner that computationally grows as a quadratic function of the number of simulated aircraft. Efficient indirect methods have been developed in certain contexts to address this limitation. However, any means of addressing separation requirements in a gradient based optimization context should be implemented by functions which provide analytic derivative information to maximize numerical precision and computational efficiency. In this paper, a fast and differentiable separation metric is described in application to gradient based optimization of airspace operations. Rather than computing the separation distance between every pair of aircraft in a simulation, this method effectively reduces the problem to a smaller relevant set using a geometric decomposition. This method guarantees that the smallest distance at all points in simulated time is determined exactly. When used in an optimization constraint context, this guarantees that a minimum separation is maintained between all pairs of aircraft. The presented metric has logarithmic computational growth with respect to the number of simulated aircraft, and is shown to perform well in a series of notional 2D airspace optimization problems when used to enforce specified airborne separation constraints. Results show that this is notably faster than a direct pairwise distance computing metric for optimizations involving both small and large numbers of aircraft, yet enforce separation requirements to the same tolerance. It is shown that this favorable scalability is an enabling capability for more sophisticated air traffic management conceptual studies.

Optimization↗
NASA NTRS2022

Preliminary Characterization of Unmanned Air Cargo Routes Using Current Cargo Operations Survey

The introduction of regional cargo unmanned aircraft systems into the National Airspace System is anticipated within the coming years. Because they are remotely piloted, these aircraft are expected to utilize increasing aircraft automation and autonomy, require special infrastructure accommodations for navigation, communication, command and control and potentially need special treatment from air traffic control. In order to assess the accessibility and impacts of these operations across the national airspace, this preliminary study investigates current and estimated future demand for air cargo operations in the continental United States. Air cargo demand is broken down by aircraft type and airport categories to produce a rough nation-wide classification of cargo operations. Then, the state of Texas is investigated as a focus region, where the impacts of regional cargo unmanned aircraft systems on the airspace are investigated in further detail. The potential technologies that can assist in regional cargo unmanned aircraft system accessibility are defined at airports across the focus region. A single airport, Fort Worth Alliance, is highlighted to discuss airport-level statistics. Finally, a qualitative classification of airports by the type of cargo operations is suggested.

Unmanned Aircraft↗
NASA NTRS2022

Preliminary Characterization of Unmanned Air Cargo Routes Using Current Cargo Operations Survey

The introduction of regional cargo unmanned aircraft systems into the National Airspace System is anticipated within the coming years. Because they are remotely piloted, these aircraft are expected to utilize increasing aircraft automation and autonomy, require special infrastructure accommodations for navigation, communication, command and control and potentially need special treatment from air traffic control. In order to assess the accessibility and impacts of these operations across the national airspace, this preliminary study investigates current and estimated future demand for air cargo operations in the continental United States. Air cargo demand is broken down by aircraft type and airport categories to produce a rough nation-wide classification of cargo operations. Then, the state of Texas is investigated as a focus region, where the impacts of regional cargo unmanned aircraft systems on the airspace are investigated in further detail. The potential technologies that can assist in regional cargo unmanned aircraft system accessibility are defined at airports across the focus region. A single airport, Fort Worth Alliance, is highlighted to discuss airport-level statistics. Finally, a qualitative classification of airports by the type of cargo operations is suggested.

Unmanned Aircraft, Unmanned Aircraft Systems, UAS,↗
NASA NTRS2022

Preliminary Analysis of Separation Standards for Urban Air Mobility using Unmitigated Fast-Time Simulation

This research provides a preliminary analysis of various separation standards for Urban Air Mobility (UAM) operations. This study focuses on understanding and analyzing potential conflicts between UAM flights and conflicts between UAM and conventional aircraft. Fast-time simulations are conducted with a projected high-density of UAM traffic scenario and historical conventional non-UAM traffic flying under Instrument Flight Rules (IFR) or Visual Flight Rules (VFR) in Dallas Fort Worth (DFW) airspace. The conflicts are analyzed by examining losses of separation and near mid-air collisions of UAM flights and unmitigated risk of collision. A set of varied separation standards are used to investigate the impact of the new UAM operations on safety. The preliminary conflict analysis results will help inform separation standards development and in airspace and route design activities for future UAM operations.

Urban air mobility↗
NASA NTRSDate not supplied

Preliminary Analysis of Separation Standards for Urban Air Mobility using Unmitigated Fast-Time Simulation

This research provides a preliminary analysis of various separation standards for Urban Air Mobility (UAM) operations. This study focuses on understanding and analyzing potential conflicts between UAM flights and conflicts between UAM and conventional aircraft. Fast-time simulations are conducted with a projected high-density of UAM traffic scenario and historical conventional non-UAM traffic flying under Instrument Flight Rules (IFR) or Visual Flight Rules (VFR) in Dallas Fort Worth (DFW) airspace. The conflicts are analyzed by examining losses of separation and near mid-air collisions of UAM flights and unmitigated risk of collision. A set of varied separation standards are used to investigate the impact of the new UAM operations on safety. The preliminary conflict analysis results will help inform separation standards development and in airspace and route design activities for future UAM operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

- NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future. - (see https://www.nasa.gov/uam-overview/ for more information) - The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). - The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and artificial intelligence capabilities. This presentation gives an overview of the Knowledge Graph and ChatGPT applications within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

systems engineering↗
NASA NTRS2023

Operational Integration Potential of Regional Uncrewed Aircraft Systems Into the Airspace System

As part of newly developing aviation markets, fixed-wing Uncrewed Aircraft Systems (UAS) are projected to impact airspace systems and conventional air traffic in the future. The initial introduction of fixed-wing cargo UAS for regional operations is anticipated to occur at smaller under-utilized airports. Therefore, this paper assesses the integration potential of regional fixed-wing cargo UAS into the airspace system. A baseline is established to identify potential airports for cargo UAS operations in different areas. Additionally, using 2022 data, regional aircraft eligible for future cargo UAS operations are investigated. Finally, the accessibility of these regional aircraft at the identified airports was analysed. Based on the availability of current certified landing systems needed for initial UAS operations, potential airports in the areas Germany, Texas, and California for UAS operations are compared. Additionally, based on the maximum takeoff weight allowances of airport runways, current air transport operations at airports, and airspace classes, individual airports with a high potential for the introduction of initial cargo UAS operations with and without the availability of landing systems needed for UAS are identified and compared among the investigated areas. Despite a total of 173 identified airports for potential UAS operations in Germany, 376 in Texas, and 231 in California, only eleven of these airports currently have the certified landing systems needed for initial UAS operations. However, other landing system technologies that are currently under development, such as vision-based landing systems, might support UAS accessibility at the identified airports for potential UAS operations in the future.

Uncrewed aircraft systems↗
NASA NTRSDate not supplied

Integrating Upper Class E Traffic Management (ETM) Operations into the National Airspace System: Use Cases and Research Questions

As new categories of vehicles are introduced in the National Airspace System, so too are novel concepts for a cooperative approach to traffic management environments. One of these new environments, Upper Class E Traffic Management (ETM), is expected to include a variety of high altitude, long endurance vehicles with a range of performance capabilities and mission profiles that operate in cooperative areas above 60,000 feet. In addition to developing the rules, architecture, and systems for operations within the ETM environment itself, it is also important to consider how ETM vehicles will integrate with traditional Air Traffic Management and interact with Air Traffic Control (ATC) as they traverse ATC-controlled airspace and transition in and out of cooperative ETM operating areas. As a first step toward future ETM demonstrations at the National Aeronautics and Space Administration (NASA) Ames Research Center’s Airspace Operations Laboratory, use cases with step-by-step procedures were developed to identify both nominal and off-nominal scenarios in which ETM operations will interact with ATC. As NASA prepares to develop a simulation platform to demonstrate ETM cooperative practices and ETM-ATC interactions, the procedures, ATC roles and responsibilities, data exchange requirements, and research questions that were identified as part of use case development will inform scenario and system architecture design. The upcoming simulation work will include initial prototype ETM-ATC coordination tools to support ATC controllers’ interactions with ETM operations. This paper will briefly discuss NASA’s upcoming ETM development work and then provide background on ETM-ATC interactions, describe each ETM-ATC interaction use case, and discuss open questions on concept, procedures, and assumptions.

ATC↗
NASA NTRSDate not supplied

Integrating Upper Class E Traffic Management (ETM) Operations into the National Airspace System: Use Cases and Research Questions

As new categories of vehicles are introduced in the National Airspace System, so too are novel concepts for a cooperative approach to traffic management environments. One of these new environments, Upper Class E Traffic Management (ETM), is expected to include a variety of high altitude, long endurance vehicles with a range of performance capabilities and mission profiles that operate in cooperative areas above 60,000 feet. In addition to developing the rules, architecture, and systems for operations within the ETM environment itself, it is also important to consider how ETM vehicles will integrate with traditional Air Traffic Management and interact with Air Traffic Control (ATC) as they traverse ATC-controlled airspace and transition in and out of cooperative ETM operating areas. As a first step toward future ETM demonstrations at the National Aeronautics and Space Administration (NASA) Ames Research Center’s Airspace Operations Laboratory, use cases with step-by-step procedures were developed to identify both nominal and off-nominal scenarios in which ETM operations will interact with ATC. As NASA prepares to develop a simulation platform to demonstrate ETM cooperative practices and ETM-ATC interactions, the procedures, ATC roles and responsibilities, data exchange requirements, and research questions that were identified as part of use case development will inform scenario and system architecture design. The upcoming simulation work will include initial prototype ETM-ATC coordination tools to support ATC controllers’ interactions with ETM operations. This paper will briefly discuss NASA’s upcoming ETM development work and then provide background on ETM-ATC interactions, describe each ETM-ATC interaction use case, and discuss open questions on concept, procedures, and assumptions.

ATC↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗