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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Proceedings of the Air Transportation Management Workshop

The Air Transportation Management (ATM) Workshop was held 31 Jan. - 1 Feb. 1995 at NASA Ames Research Center. The purpose of the workshop was to develop an initial understanding of user concerns and requirements for future ATM capabilities and to initiate discussions of alternative means and technologies for achieving more effective ATM capabilities. The topics for the sessions were as follows: viewpoints of future ATM capabilities, user requirements, lessons learned, and technologies for ATM. In addition, two panel sessions discussed priorities for ATM, and potential contributions of NASA to ATM. The proceedings contain transcriptions of all sessions.

Tobias, Leonard↗
NASA NTRS2007

Comprehensive Software Eases Air Traffic Management

To help air traffic control centers improve the safety and the efficiency of the National Airspace System, Ames Research Center developed the Future Air Traffic Management Concepts Evaluation Tool (FACET) software, which won NASA's 2006 "Software of the Year" competition. In 2005, Ames licensed FACET to Flight Explorer Inc., for integration into its Flight Explorer (version 6.0) software. The primary FACET features incorporated in the Flight Explorer software system alert airspace users to forecasted demand and capacity imbalances. Advance access to this information helps dispatchers anticipate congested sectors (airspace) and delays at airports, and decide if they need to reroute flights. FACET is now a fully integrated feature in the Flight Explorer Professional Edition (version 7.0). Flight Explorer Professional offers end-users other benefits, including ease of operation; automatic alerts to inform users of important events such as weather conditions and potential airport delays; and international, real-time flight coverage over Canada, the United Kingdom, New Zealand, and sections of the Atlantic and Pacific Oceans. Flight Explorer Inc. recently broadened coverage by partnering with Honeywell International Inc.'s Global Data Center, Blue Sky Network, Sky Connect LLC, SITA, ARINC Incorporated, Latitude Technologies Corporation, and Wingspeed Corporation, to track their aircraft anywhere in the world.

Source record↗
NASA NTRS2004

Air System Information Management

I flew to Washington last week, a trip rich in distributed information management. Buying tickets, at the gate, in flight, landing and at the baggage claim, myriad messages about my reservation, the weather, our flight plans, gates, bags and so forth flew among a variety of travel agency, airline and Federal Aviation Administration (FAA) computers and personnel. By and large, each kind of information ran on a particular application, often specialized to own data formats and communications network. I went to Washington to attend an FAA meeting on System-Wide Information Management (SWIM) for the National Airspace System (NAS) (http://www.nasarchitecture.faa.gov/Tutorials/NAS101.cfm). NAS (and its information infrastructure, SWIM) is an attempt to bring greater regularity, efficiency and uniformity to the collection of stovepipe applications now used to manage air traffic. Current systems hold information about flight plans, flight trajectories, weather, air turbulence, current and forecast weather, radar summaries, hazardous condition warnings, airport and airspace capacity constraints, temporary flight restrictions, and so forth. Information moving among these stovepipe systems is usually mediated by people (for example, air traffic controllers) or single-purpose applications. People, whose intelligence is critical for difficult tasks and unusual circumstances, are not as efficient as computers for tasks that can be automated. Better information sharing can lead to higher system capacity, more efficient utilization and safer operations. Better information sharing through greater automation is possible though not necessarily easy.

Filman, Robert E.↗
NASA NTRSDate not supplied

Trajectory Simulation for Air Traffic Management Employing a Multirotor Urban Air Mobility Aircraft Model

This paper describes a point-mass model of a multirotor electric vertical takeoff and landing aircraft designed to carry up to five passengers and a pilot. The model employs thrust, thrust vector angle and bank angle as controls, which are computed in part using the acceleration, heading-angle rate, and flight-path-angle rate generated by control systems in response to speed, heading angle, and flight-path angle commands. The equations of motion are integrated forward in time using the controls needed for following the desired vertical climb/descent and speed profiles, and horizontal path and cruise speed in the presence of wind to generate the flight trajectory.

trajectory simulation, urban air mobility aircraft↗
NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗
NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗
NASA NTRS2019

Cyber Physical Security (CPS) Extension to Air Traffic Management (ATM) Testbed

The Air Traffic Management (ATM) Testbed is being developed at NASA to enable benefit, impact, safety and cost assessments for accelerating the deployment of Concept and Technologies (C&T) in the National Airspace System (NAS). Today, C&T introduction into the NAS takes decades. The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems (Realistic Technologies) in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airline fleet operations. Transition of C&T to operations requires mathematical modeling and simulation, Human-in-the-Loop (HITL) testing and shadow-mode evaluation driven by operational data. Whereas interaction with the operational system during testing and stages of deployment is not permissible due to safety concerns, it is certainly possible to create a simulation environment that closely mimics the NAS using the same operational systems/hardware for enabling such assessments. This presentation focuses on a proposed Cyber Physical Security extension to the ATM Testbed for creating a modeling and simulation architecture to study how well the Air Traffic Management system will perform and analyze effectiveness of mitigating security measures against particular cyber-attack scenarios.

Datta, Koushik↗
NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗
NASA NTRS2022

Air Traffic Management TestBed: Messaging Performance

The Air Traffic Management (ATM) TestBed is an air traffic management modeling and simulation platform and framework developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The communication middleware, implemented in the TestBed framework layer, is a core feature for data message exchange. The feature provides an abstraction layer called Messaging Support to allow switching one middleware to another without a need to rebuild the simulation components. Messaging performance such as latencies, run durations, and throughputs are important factors. Low latencies can produce accurate results in high-fidelity and visualization models. Short run durations are preferred because better run efficiency can be achieved. High throughputs allow more runs to be executed concurrently. This technical memorandum studies and compares the messaging performance by running a full-day, fast-time simulation using three communication middleware as well as tweaking the default communication middleware settings used by the TestBed. Results indicate that the messaging performance could be improved by disabling either compression or persistence settings, while the run duration and throughput could be further improved by disabling both settings with a tradeoff of the message latencies increased by a factor of ten.

Chok Fung Lai↗
NASA NTRS2024

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗
NASA NTRS2017

Airborne Trajectory Management (ABTM): A Blueprint for Greater Autonomy in Air Traffic Management

The aviation users of the National Airspace System (NAS) - the airlines, General Aviation (GA), the military and, most recently, operators of Unmanned Aircraft Systems (UAS) - are constrained in their operations by the design of the current paradigm for air traffic control (ATC). Some of these constraints include ATC preferred routes, departure fix restrictions and airspace ground delay programs. As a result, most flights cannot operate on their most efficient business trajectories and a great many flights are delayed even getting into the air, which imposes a significant challenge to maintaining efficient flight and network operations. Rather than accepting ever more sophisticated scheduling solutions to accommodate the existing constraints in the airspace, a series of increasingly capable airborne technologies, integrated with planned improvements in the ground system through the Federal Aviation Administration (FAA) Next Generation Air Traffic Management System (NextGen) programs, could produce much greater operational flexibility for flight path optimization by the aviation system users. These capabilities, described in research coming out of NASA's Aeronautics Research Mission Directorate, can maintain or improve operational safety while taking advantage of air and ground NextGen technologies in novel ways. The underlying premise is that the nation's physical airspace is still abundant and underused, and that the delays and inefficient flight operations resulting from artificial structure in airspace use and procedural constraints on those operations may not be necessary for safe and efficient flight. This article is not an indictment of today's NAS or the people who run it. Indeed, it is an exceptional achievement that Air Traffic Management (ATM) - the complex human/machine conglomeration of communications, navigation and surveillance equipment and the rules and procedures for controlling traffic in the airspace - has both the capacity and enables the degree of efficiency in air travel that it does. But it is also true that sixty years of the "radar religion" (i.e., reliance on radar-based command and control) has produced several generations of ATM system operators and researchers who believe that introducing automation within the existing functional structure of ATM is the only way to "modernize" the system. Even NextGen, which began as a proposal for "transformational" change in the way ATC is performed, has morphed over the last decade and a half to become just the inclusion of Global Positioning System (GPS) for navigation, Automatic Dependent Surveillance Broadcast (ADS-B) for surveillance, and Data Communications (Data Comm) for communications, while still operating in rigidly structured airspace with human controllers being responsible for separation and traffic flow management (TFM) within defined sectors of airspace, using the same horizontal separation standards that have been in use since raw primary radar was introduced in the 1950s. No system as massive as the current NAS ATM can be replaced with a better system while simultaneously meeting the transportation and other aviation needs of the nation. A new generation of more flexible operations must emerge and yet coexist in harmony with the current operation (i.e., share the same airspace without segregation), thereby enabling a long-term transformation to take place in the way increasing numbers of flights are handled. Market forces will be the ultimate driver of this transformation, and investment realities mandate that real benefits must accrue to the first operators to adopt these new capabilities. In fact, the kinds of missions envisioned in the emerging world of UAS operations, unachievable under conventional ATM, demand that this transformation take place. Airborne Trajectory Management (ABTM) is proposed as a series of transformational steps leading to vastly increased flexibility in flight operations and capacity in the airspace to accommodate many varied airspace uses while improving safety. As will be described, ABTM enables the gradual emergence of a new paradigm for user-based trajectory management in ATM that brings tangible benefits to equipped operators at every step while leveraging the air and ground investments of NextGen. There are five steps in this ABTM transformation.1 NASA has extensively studied the first and last of these steps, and a roadmap of increasing capabilities and benefits is proposed for bridging between these operational concepts.

Cotton, William B.↗
NASA NTRS1998

Airspace Complexity and its Application in Air Traffic Management

The United States Air Traffic Management (ATM) system provides services to enable safe, orderly and efficient aircraft operations within the airspace over the continental United States and over large portions of the Pacific and Atlantic Oceans, and the Gulf of Mexico. It consists of two components, Air Traffic Control (ATC) and Traffic Flow Management (TFM). The ATC function ensures that the aircraft within the airspace are separated at all times while the TFM function organizes the aircraft into a flow pattern to ensure their safe and efficient movement. In order to accomplish the ATC and TFM functions, the airspace over United States is organized into 22 Air Route Traffic Control Centers (ARTCCs). The Center airspace is stratified into low-altitude, high-altitude and super-high altitude groups of Sectors. Each vertical layer is further partitioned into several horizontal Sectors. A typical ARTCC airspace is partitioned into 20 to 80 Sectors. These Sectors are the basic control units within the ATM system.

Sridhar, Banavar↗
NASA NTRS2017

Results from an Interval Management (IM) Flight Test and Its Potential Benefit to Air Traffic Management Operations

NASA's first Air Traffic Management Technology Demonstration (ATD-1) subproject successfully completed a 19-day flight test of an Interval Management (IM) avionics prototype. The prototype was built based on IM standards, integrated into two test aircraft, and then flown in real-world conditions to determine if the goals of improving aircraft efficiency and airport throughput during high-density arrival operations could be met. The ATD-1 concept of operation integrates advanced arrival scheduling, controller decision support tools, and the IM avionics to enable multiple time-based arrival streams into a high-density terminal airspace. IM contributes by calculating airspeeds that enable an aircraft to achieve a spacing interval behind the preceding aircraft. The IM avionics uses its data (route of flight, position, etc.) and Automatic Dependent Surveillance-Broadcast (ADS-B) state data from the Target aircraft to calculate this airspeed. The flight test demonstrated that the IM avionics prototype met the spacing accuracy design goal for three of the four IM operation types tested. The primary issue requiring attention for future IM work is the high rate of IM speed commands and speed reversals. In total, during this flight test, the IM avionics prototype showed significant promise in contributing to the goals of improving aircraft efficiency and airport throughput.

Baxley, Brian↗
DOE OSTI2024

Zero Emission Cargo Transport (ZECT) II Demonstration: South Coast Air Quality Management District (Final Report)

The South Coast Air Quality Management District (South Coast AQMD), California Air Resources Board (CARB) and Southern California Association of Governments (SCAG) — the agencies responsible for preparing the State Implementation Plan required under the federal Clean Air Act — have agreed that attainment of federal air quality standards for the region will require a transition to the broad use of zero and near-zero emission energy sources in cars, trucks and other equipment. Accordingly, the 2012 South Coast AQMD Air Quality Management Plan, the SCAG 2012 Regional Transportation Plan, and the “Vision for Clean Air: A Framework for Air Quality and Climate Control Planning” all identify the need to immediately enact a phasing in of zero and near-zero emission technologies to meet air quality goals. In 2014, South Coast AQMD was awarded grant funding under the US Department of Energy Zero Emission Cargo Transport (ZECT) II Demonstration program to develop and demonstrate zero-emission drayage trucks for goods movement operations between the Port of Los Angeles (POLA) and Port of Long Beach (POLB) near dock rail yards and warehouses: 1) development and demonstration of zero-emission fuel cell range extended electric drayage trucks and 2) development and demonstration of hybrid electric drayage trucks. The purpose of this project was to accelerate deployment of zero emission cargo transport technologies to reduce harmful diesel emissions, petroleum consumption and greenhouse gases in the surrounding communities along the goods movement corridors that are impacted by heavy diesel traffic and the associated air pollution. Between 2014 – 2024, six ZECT II zero-emission fuel cell drayage truck platforms, including fuel cell range extended and CNG hybrid trucks, were successfully designed, developed, integrated, built, tested, and demonstrated with drayage fleet operators in transportation corridors within areas of the South Coast AQMD jurisdiction in Southern California such as in and around POLA and POLB. Portable hydrogen refueling was deployed to support the fuel cell vehicles. The project had real-time improvement with on-going debugging and optimizations while the vehicles were under demonstration. All platforms demonstrated sufficient or excess power, torque, and energy to support 82,000lbs Gross Vehicle Weight Rating and gradeability to perform their daily duty cycles. Collectively, the trucks drove over 23,000 miles during their respective demonstration phases. The ZECT II project was the first of its kind to demonstrate the commercial viability that supported the additional technology breakthroughs for Class 8 zero emission trucks and validations as well as the regulatory basis for all the zero-emission regulation that we know today, such as the Innovative Clean Transit regulation, Advanced Clean Trucks and Clean Fleet regulations.

08 HYDROGEN↗
NASA NTRSDate not supplied

Development of TEMPO Products and Tools to Support Air Quality Management Decisions

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. Our project at the NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on the air quality management community. The initial focus of this project is evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. Our SPoRT viewer has been updated to include visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air quality management stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data in air quality management applications. The major outcome of this project is to support the inclusion of TEMPO data in exceptional event demonstrations by active engagement with stakeholders and ultimately enable more informed air quality management decisions in the future. This talk will provide an update on our project activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

air quality↗
NASA NTRS2017

The NASA Air Traffic Management Ontology: Technical Documentation

This document is intended to serve as comprehensive documentation for the NASA Air Traffic Management (ATM) Ontology. The ATM Ontology is a conceptual model that defines key classes of entities and relationships pertaining to the US National Airspace System (NAS) and the management of air traffic through that system. A wide variety of classes are represented in the ATM Ontology, including classes corresponding to flights, aircraft, manufacturers, airports, airlines, air routes, NAS facilities, air traffic control advisories, weather phenomena, and many others. The Ontology can be useful in the context of a variety of information management tasks relevant to NAS, including information exchange, data query and search, information organization, information integration, and terminology standardization.

information management↗
NASA NTRS2016

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↗
NASA NTRS2016

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↗