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This presentation provides lessons learned for use of ramp traffic control (RTC) and ramp manager traffic console (RMTC) features of the ATD-2 (Airspace Technology Demonstration 2) Integrated Arrival, Departure, and Surface (IADS) system in an operational environment.
The Scalable Traffic Management for Emergency Response Operations (STEReO) project aims to apply various NASA technologies, such as Unmanned Aircraft System (UAS) Traffic Management (UTM) services, onboard-vehicle autonomy, novel approaches to communications and connectivity, and remote/virtual collaboration interfaces to current-day emergency response efforts to natural disasters. Today's emergency response efforts are based on long-standing procedures that are manaul in nature, and could benefit from modernization. In particular, the use of UAS vehicles is limited, primarily due to concerns surrounding dangerous interactions between manned/unmanned operations. STEReO hopes to introduce data exchanges that improve shared situation awareness, reduce manual coordination procedures, enable scalable and high-density air operations, collectivley bringing a positive impact to the incident response. When successful, STEReO technologies will improve efficiency and timeliness of the response and recovery phases of a disaster, resulting in substantial reductions to community harm, and will also accelerate NASA’s development of high-density resilient operations, benefitting other projects relying on increasing levels of autonomy and connectivity. The prepared material provides an overview of the STEReO project, for presentation at the 2019 Convergent Aeronautics Solution (CAS) Showcase event. The CAS showcase brings together all of the current CAS execution activities, highlighting NASA's high-risk investments in aeronautics research.
IAI's MetroSim optimizes air traffic by simulating departures, arrivals, and activity in air and onthe ground in busy metroplexes, where flights impact each other at a single airport and among traffic at nearby airports. MetroSim evolved out of several NASA SBIR/STTR Awards and has since been used by NASA for flight simulation analysis. MetroSim has also been integrated with FAA and DOT technology, has produced studies for the Port Authority of New York and New Jersey, and is under development to support the Navy
STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the STEReO project to attendees of the AIAA Aviation conference.
This is a slide set to be used as part of a meeting series with members of a working group aimed at the development of a concept that addresses needs and gaps in the management of high altitude airspace operations. This concept leverages elements developed through the UAS Traffic Management project with respect to a cooperative, service-based approach that provides services and capabilities in areas (e.g., Upper Class E airspace) that currently receive no or limited service from Air Traffic Control. This concept is meant to provide a safe, fair, and scalable approach to management of Upper Class E operations that reduces the burden on ATC while providing the flexibility and access desired by current and new users of the airspace. This set of slides includes an overview of the past accomplishments, current project structure, and a simulation roadmap for planned work ahead.
The National Aeronautics and Space Administration is supporting research to transition from the current air traffic management paradigm to Trajectory-Based Operations targeting the 2035-2045 timeframe. Trajectory-Based Operations in the National Airspace System focuses on modernizing current operations and technology to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. To understand the impact of new technologies and procedures that enable Trajectory-Based Operations, simulation studies must be conducted using realistic scenarios. This paper describes techniques that can be employed when using recorded airspace traffic data to create scenarios representative of a 2040 Trajectory-Based Operations airspace. The paper provides a use case example and suggests how this methodology may be applied to other simulations studying concepts, technologies, and procedures in a future airspace system.
Within the aviation domain, there is a growing industry demand to develop and integrate remotely piloted operations into the National Airspace System. However, it is not yet well understood how the integration of unmanned aircraft with impact air traffic control, and specifically, the air traffic controllers who are at the sharp end of this safety critical system. This research presented in this paper aimed to begin to address this gap in understanding by identifying and exploring potential hazards associated with introducing Unmanned Aircraft into the national airspace system, and identify possible mitigations to reduce identified risks. A bowtie risk analysis methodology was used to identify and analyze hazards. A focus-group format discussion was conducted with nine subject matter experts as participants. Findings identified five areas of potential risk, each associated with multiple hazards. Mitigations for each hazard are reported. Findings have essential implications for the safe and efficient integration of unmanned aircraft into the national airspace.
Running realistic simulations and developing algorithms for decision support tools usually require weather data access. This document describes the weather data service capability implemented in the Air Traffic Management (ATM) TestBed. The capability provides ways for querying weather data from the weather products provided by the National Aeronautics and Space Administration’s (NASA) Sherlock ATM Data Warehouse. Depending on use cases, weather data can be queried using a Java application programming interface, web data access, and map tile image access. Map tile images are generated using triangular meshes obtained using the Matching Squares contour algorithm. An analysis determines a grid size that will produce both high image quality and fast image rendering. In addition, the visualization of weather data as map tiles on a TestBed visualization tool called Traffic Viewer is presented.
A barrier to the integration of remotely piloted aircraft operations in the U.S. National Airspace System is the latency of voice communications between the air traffic controller and the remote pilot, and the latency of communication between the aircraft and the remote pilot. The latency can be substantial especially when satellite-based beyond-radio-line-of-sight communication and relay through the aircraft are employed. This study uses voice recordings of controller-pilot communications and aircraft track data to establish a baseline of pilot readback latencies and maneuver detection delays in the current piloted operations. A machine learning pipeline was developed to parse the contents of the air traffic control clearances including the callsigns using natural language processing. After manually validating the results obtained using the pipeline, the average pilot readback latency was found to be about 0.6 seconds. The average latency between the end of maneuver (inferred from track data), initiated by the pilot in response to the clearance, and the end of clearance was found to be about 176 seconds for altitude change commands, 69 seconds for heading change commands, and 182 seconds for speed change commands. The average latency between the beginning of maneuver and the end of clearance was found to be about 17 seconds for altitude change commands, 17seconds for heading change commands, and 25 seconds for speed change commands.
A barrier to the integration of remotely piloted aircraft operations in the U.S. National Airspace System is the latency of voice communications between the air traffic controller and the remote pilot, and the latency of communication between the aircraft and the remote pilot. The latency can be substantial especially when satellite-based beyond-radio-line-of-sight communication and relay through the aircraft are employed. This study uses voice recordings of controller-pilot communications and aircraft track data to establish a baseline of pilot readback latencies and maneuver detection delays in the current piloted operations. A machine learning pipeline was developed to parse the contents of the air traffic control clearances including the callsigns using natural language processing. After manually validating the results obtained using the pipeline, the average pilot readback latency was found to be about 0.6 seconds. The average latency between the end of maneuver (inferred from track data), initiated by the pilot in response to the clearance, and the end of clearance was found to be about 176 seconds for altitude change commands, 69 seconds for heading change commands, and 182 seconds for speed change commands. The average latency between the beginning of maneuver and the end of clearance was found to be about 17 seconds for altitude change commands, 17seconds for heading change commands, and 25 seconds for speed change commands.
STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This poster gives an informational overview of the STEReO project to attendees of the imaginAviation event.
Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide range of applications such as cinematography, package delivery, and surveying. As a result, regulators have become interested in developing UAV Traffic Management (UTM) systems to coordinate UAV traffic. One possible framework for UTM is a combinatorial auction. Under this framework, airspace is modeled as a grid of space-time cells. UAV operators bid on sets of cells which collectively form flight paths for their UAVs. An ideal airspace auction should: be fair, be incentive-aligned, be scalable, allocate airspace economically-efficiently, enable price discovery, and reduce the work required to participate where possible. In this paper, we propose the first auction mechanism for airspace allocation that meets the criteria above. Our mechanism: (a) is provably economically-efficient, fair and incentive-aligned, (b) shares pricing information with bidders and (c) has features which reduce the burden of participating. We evaluate our mechanism on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to 26,000 bids.
In the past, operations at high altitudes have been limited in number and largely conducted for defense/security purposes. However, with broad technological advances, a growing number of commerical and public good use cases, and a significant rise in the number of capable platforms, the demand for operating in high altitude airspace - known as upper Class E in the United States - is increasing. With that increase in demand comes the need for a means to manage the airspace in a way that does not burden current air traffic services and infrastructure given that provisions for commercial operations are limited. NASA, in collaboration with other government agencies and strong representation from industry, has developed a cooperative approach to airspace management referred to as Upper Class E Traffic Management (ETM). Key aspects of the ETM concept are the ability to exchange information through services that enable the operators to share airspace in cetain areas by adhering to cooperative operating practices (COPs) and by having shared situation awareness. To advance the concept, NASA recently developed and formally tested the first dedicated reference ETM system and supportin architecture. The test involved real-time simulation of high altitude operations with a diverse set of aircraft and encourter situations that included industry partners connected to the system from their remote operating centers. This presentation will provide attendees with an understanding of the ETM concept, the details and importance of the system that has been developed, an overview of the groundbreaking simulation, and a glimpse of things to come in our next steps.
Traffic Management Initiatives (TMIs) play a crucial role in balancing demand and capacity within the U.S. National Airspace System (NAS). In current practice, traffic management coordinators (TMCs) determine and issue TMIs and recent research has explored the use of machine learning tools to aid the TMCs. However, most studies have primarily focused on a particular type of TMI, i.e., Ground Delay Programs (GDPs) due to their higher rate of occurrence and longer duration. This study investigates a machine learning approach for monitoring and adjusting a different type of TMI, i.e., Ground Stop (GS), aiming to assist human decision-makers with accurate, consistent, and timely recommendations. Using data from three major airports in the New York metroplex, we evaluated models that predict GS parameters, such as duration and scope. Our results demonstrate that using data from all airports in the NY metroplex and increasing feature granularity improve the prediction accuracy of the ML models.
The use of hydrogen as an alternative transportation fuel has gained much interest in recent years. Hydrogen can be utilized in electric vehicles equipped with hydrogen powertrains (including hydrogen internal combustion engines or fuel cells). Given that most of the freight in the U.S. is transported via diesel trucks, transition to hydrogen fuel would help achieve significant environmental benefits as well as accelerate the decarbonization of the freight transportation sector. This paper presents the methodology and results of a case study on modeling freight traffic demand and highway networks based on publicly available data for the Interstate 75 freight corridor. The purpose of this study is to prepare input traffic and network data that can support the planning of a hydrogen fueling station infrastructure. In particular, the data can be used for siting and characterizing an optimized framework of hydrogen fueling stations from candidate diesel stations along the Interstate 75 corridor. The methodologies developed and presented in this paper may be readily expanded and applied to any transport corridor given the data availability. This paper is the first in a series that will build out a comprehensive model to optimize a consolidated national hydrogen refueling infrastructure eco-system targeted at commercial vehicles.
The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.
In the present work we model multi-lane traffic flow in presence of two population of vehicles: cars and trucks. We first develop a finite-dimensional hybrid system which rely on continuous Bando-Follow-the-Leader dynamics coupled with discrete events motivated by the lane-change maneuvers. Then we rigorously prove that the mean-field limit is given by a system of Vlasov-type PDE with source terms generated by the lane-change maneuvers of the human-driven vehicles.
This study evaluates a traffic management concept designed to enable simultaneous operations of multiple small unmanned aircraft systems (UAS) in the national airspace system (NAS). A five-day flight-test activity is described that examined the feasibility of operating multiple UAS beyond visual line of sight (BVLOS) of their respective operators in the same airspace. Over the five-day campaign, three groups of five flight crews operated a total of eleven different aircraft. Each group participated in four flight scenarios involving five simultaneous missions. Each vehicle was operated BVLOS up to 1.5 miles from the pilot in command. Findings and recommendations are presented to support the feasibility and safety of routine BVLOS operations for small UAS.