National Airspace System Research Articles

Building interfaces between unmanned aircraft systems (UAS), air traffic controllers (ATCo), and the national airspace system (NAS): A software training platform

Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.

Leadership and Next Generation Unmanned System Integration

The purpose of this study is to explore the challenges and successes Aerospace Industry leaders encounter regarding the safe integration of unmanned aerial vehicles (UAVs) that weigh over 55 lbs. into the National Airspace System (NAS). Specifically, how will leadership in the Aerospace Industry respond to a rogue UAV flying in the National Airspace when the communication link is lost due to hardware or software failure. A qualitative intrinsic case study was used for this study. Using snowball sampling, a total of 15 participants responded to 12 open-ended survey questions. The findings presented challenges the Aerospace Industry must overcome for larger UAVs to fly in the national airspace. The major themes identified were: (a) Issues with integration efforts between the FAA and the Aerospace Industry, (b) Safety in the national airspace for all aircraft, and (c) Progress and Challenges. The study recommends that the Aerospace Industry consider implementing Industry 4.0 technology into their business, examining the UAV flight management system, and a redesigning of the COA process.

Realizing a 21st Century Noise Policy

Aviation noise impacts affect the health and quality of life of communities nationwide. The FAA's noise policy, last updated in the 1970s, uses a single decision-making metric (DNL), to determine the significance of noise impacts caused by aircraft operations. The Neighborhood Environmental Survey (NES), released in 2021, shows that many more people are impacted by aircraft noise and at levels far below 65 dB DNL than previously thought. The current noise policy does not reflect the 21st century airspace environment, including the consequences of NextGen and the tremendous growth in air traffic. An important improvement to realize an up-to-date noise policy is to reflect the lived experience of impacted communities more accurately. This paper will cover how communities experience aircraft noise in two separate noise exposure environments both near and away from airports, which practices are issues and most concerning to communities, what metrics more accurately evaluate impacts, and how a more community-centric approach provides a better representation of the aviation noise experienced by people, which is fundamental to operating a National Airspace System that works for all.

Navigating Massive Text Reports: An Automated Approach to Aviation Safety Reporting System Safety Event Detection

In response to the increasing report intake and the need for more efficient and effective safety event detection and monitoring in the national airspace system (NAS), this work proposes an automated and sustainable workflow that collectively applies natural language processing (NLP) techniques to the Aviation Safety Reporting System (ASRS) report narratives. The proposed workflow uses latent Dirichlet allocation (LDA), a probabilistic model for uncovering hidden topics within text documents, to perform topic modeling and constructs topic trajectories based on Document Frequency – Inverse Document Frequency [Formula: see text]. By applying spectral analysis to the constructed trajectories, we identify multiple periodic and aperiodic topics in unfiltered ASRS report narratives from 2011 to 2021 with minimal human input. Through validation, we confirm that the periodic and aperiodic topics detected can be linked to actual safety events trending in the aviation community and are valuable for safety improvement. Beyond existing topic modeling applications on ASRS reports, this work could deliver more relevant and practical information to safety management experts, not only filling the gap between the topic model output and practical applications but also providing safety management experts with a holistic view of the entire report intake to oversee commonalities. The proposed workflow, with appropriate modifications, has the potential to be adapted to other safety reporting systems.

Deep-Learning Framework for Terminal Airspace Trajectory Prediction and In-Time Prognostics

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System. To prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatiotemporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep-learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 min into the future and estimate prognostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport.

Operational wind and turbulence nowcasting capability for advanced air mobility

The present study introduces “WindAware”, a wind and turbulence prediction system that provides nowcasts of wind and turbulence parameters every 5 min up to 6 h over a predetermined airway over Chicago, Illinois, USA, based on 100 m high-resolution simulations (HRSs). This system is a long short-term memory-based recurrent neural network (LSTM-RNN) that uses existing ground-based wind data to provide nowcasts (forecasts up to 6 h every 5 min) of wind speed, wind direction, wind gust, and eddy dissipation rate to support the Uncrewed Aircraft Systems (UASs) safe integration into the National Airspace System (NAS). These HRSs are validated using both ground-based measurements over airports and upper-air radiosonde observations and their skill is illustrated during lake-breeze events. A reasonable agreement is found between measured and simulated winds especially when the boundary layer is convective, but the timing and inland penetration of lake-breeze events are overall slightly misrepresented. The WindAware model is compared with the classic multilayer perceptron (MLP) and the eXtreme Gradient Boosting (XGBoost) models. It is demonstrated by comparison to high-resolution simulations that WindAware provides more accurate predictions than the MLP over the 6 h lead times and has almost similar performance as the XGBoost model although the XGBoost’s training is the fastest using its parallelized implementation. WindAware also has higher prediction errors when validated against lake-breeze events data due to their under-representation in the training dataset.

Collision Avoidance Capabilities in High-Density Airspace Using the Universal Access Transceiver ADS-B Messages

The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.

Prediction of Hourly Airport Operational Throughput with a Multi-Branch Convolutional Neural Network

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations.

Overtaking collision avoidance for small autonomous uncrewed aircraft using geometric keep-out zones

Autonomous uncrewed aircraft will require collision avoidance systems (CASs) designed with autonomy in mind as they integrate into the increasingly crowded national airspace system. Current uncrewed aircraft CASs typically require a remote pilot to execute avoidance or to provide poorly defined guidance that does not benefit autonomous systems. The Path Recovery Automated Collision Avoidance System (PRACAS) re-plans flight paths to autonomously adjust for collisions using path planners and keep-out zones (KOZs), but it does not currently detect or mitigate overtaking collisions. This work investigates the effect of geometric KOZs on overtaking scenarios for autonomous uncrewed aircraft. KOZ shapes were developed by relating relative velocities and turn rates of aircraft in overtaking scenarios and were tested using PRACAS. The operational ranges for approach heading, relative velocity, and look-ahead time were then determined. The set of KOZs that were developed prevented intruder aircraft from entering the minimum separation distance of one wingspan from the mission aircraft in overtaking scenarios with look-ahead times between 5 and 12 s, relative velocities of 2–20, and approach angles between 110° and −110° measured from the heading of the main UAS. Minimum separation was maintained for low-speed encounters with relative velocities between 1.1 and 2.0 for look-ahead times between 2 and 8 s for all approach angles. With look-ahead times ranging from 5 to 8 s, overtaking collisions of all tested approach angles and relative speeds are handled with more than twice the separation required for success, showing that the KOZs developed are feasible in possible autonomous CASs.

An examination of sUAS operations in proximity to a major U.S. Airport

The use of small, Unmanned Aircraft Systems (sUAS) has proliferated in the past several years. While operators are supposed to avoid operations in the immediate vicinity of airports, there are sometimes violations. In extreme cases, these violations can result in the complete grounding of flights at major airports around the world. These disruptions can lead to thousands of flights canceled and hundreds of thousands of passengers displaced. A compounding problem is the lack of empirical data to understand the amount of, and types of, sUAS operations around major airports. The purpose of this study was to assess the characteristics of sUAS operations near the Dallas-Fort Worth International Airport (DFW) by objectively analyzing data collected over a 36-month timeframe. The findings provide empirical data on the types of sUAS operations around DFW, yearly variance in operations, levels of UAS facility map compliance, types and times of sUAS flown, and sUAS launching points. The data indicates a growing trend in operations between 2018 and 2020, with a possible plateau in 2021 and beyond. sUAS operations in the airport vicinity are high, with approximately 70 % operating in the 400-foot grid levels or at points farther away from critical airport operations. Flights appear to follow seasonality and daily operation patterns, and growth seems to be in smaller vehicles with weights less than 250 g. In addition to the results, the authors discuss practical implications for National Airspace System (NAS) safety and the implications of integrating new operations into communities, such as drone deliveries and Advanced Air Mobility operations.

Validation of a new method for designing air traffic control alarms

Alarms, alerts, and warnings are critical to maintaining safety in the National Airspace System and should be designed to support aircraft separation as well as supplementary tasks such as weather avoidance. The purpose of this study is to validate a novel alarm design framework by asking air traffic controllers to evaluate an existing alarm. MethodsWe invited four air traffic controllers to participate in a structured interview that is part of a novel Signal Design Framework. Controllers were asked a series of scripted questions about 15 specific alarm properties. They were then asked to choose the three properties most important to the design of the conflict alert. Lastly, controllers were asked a series of questions about the overall quality of the taxonomy and its potential for impacting aviation safety. ResultsAll participants agreed that the taxonomy captured all the important characteristics of an alarm and that no gaps or failures existed in the alarm framework. They also agreed that the framework was easy to understand, that the structured interview was easy to understand, and that applying the framework to alarm design and revision would improve alarm ease of use, reduce confusion, and improve overall safety. ConclusionsThe structured interview encouraged controllers to think about the Conflict Alert and helped them to develop novel solutions that could potentially improve this alarm in the Air Traffic Control environment.

The Role of Human Factors in Evaluating and Updating Requirements for Flight Deck Displays and Controls

The remarkable level of safety of aviation operations is due, in part, to Federal Aviation Administration requirements and guidance that govern civilian aviation activities in the United States National Airspace System. At times, these requirements and guidelines need reevaluation and updating—for example, due to a known change in the pilot workforce or with flight deck technology. This paper provides examples of when this may be necessary, and explains the role that human factors research plays in this process. One objective of this paper is to make the case that it is necessary to periodically review and update requirements and guidance, even if the equipment or technology haven’t changed; often the human operators have changed or additional information is now available. Additionally, the process described here may be applicable to international aviation authorities, other federal agencies, and the private sector, as a best practice and critical safety initiative.

Advanced Air Mobility: Systematic Review of Human Factors’ Scientific Publications and Policy

When new entrants invade an overlooked segment of the market, they can overtake established incumbents by providing innovative technologies or solutions in a small way. These disruptive innovations can grow to be highly lucrative markets in their own right (Bower, J. L., and Clayton, M. C., “Disruptive Technologies: Catching the Wave,” Harvard Business Review, Vol. 73, No. 1, 1995, pp. 43–53). One such disruptive innovation is advanced air mobility (AAM), which represents the diversity of operations using advanced vehicles with varying levels of autonomy and technologies. These operations will present unique challenges to integration in the National Airspace System. The goal of this research was to conduct a systematic review of AAM-related human factor publications, categorize human factor research areas, delineate issues, and identify gaps where future research can be focused. Findings in the current study identified qualifications, roles, and responsibilities where future research would be crucial to inform policy and standardization of regulations.

Probability of Trajectory Deviation of Unmanned Aerial Vehicle in Presence of Wind

Incorporating unmanned aerial vehicles (UAVs) into the United States National Airspace System would demand enhanced airspace safety technologies for the safety of the UAVs, people, and property on the ground. One of the safety-critical factors to consider is the risk of a UAV deviating from its planned trajectory, which may result in loss of separation between other vehicles or obstacles or may cause early depletion of battery power. In this paper, we studied the effect of wind on UAV trajectory deviation by incorporating wind velocity as a drag component in a six-degrees-of-freedom trajectory simulation comprising a rotorcraft lumped-mass model. Both steady-state wind and wind turbulence effects were investigated. We validated our approach using real flight data from UAV experiments conducted at NASA Langley Research Center. The proposed approach would enable risk-informed decision making by timely mitigation of current and future collision events in an uncertain and dynamic environment.

Three Case Studies on Small Uncrewed Aerial Systems Near Midair Collisions with Aircraft: An Evidence-Based Approach for Using Objective Uncrewed Aerial Systems Detection Technology

<div>Small uncrewed aircraft systems (sUAS) growth continues for recreational and commercial applications. By 2025, the Federal Aviation Administration (FAA) predicts the sUAS fleet to number nearly 2.4 million units. As sUAS operations expand within the National Airspace System (NAS), so too does the probability of near midair collisions (NMACs) between sUAS and aircraft. Currently, the primary means of recognizing sUAS NMACs rely on pilots to visually spot and evade conflicting sUAS. Pilots may report such encounters to the FAA as UAS Sighting Reports. Sighting reports are of limited value as they are highly subjective and dependent on the pilot to accurately estimate range and altitude information. Moreover, they do not account for NMACs that an aircrew member does not spot. The purpose of this study was to examine objective sUAS and aircraft telemetry data collected using a DJI Aeroscope sensor and Automatic Dependent Surveillance-Broadcast (ADS-B)/Mode S messages throughout 36 months near a major United States (U.S.) airport. This data offers objective insights into the interaction of sUAS and aircraft in the airspace surrounding this airport. Using the data, three NMAC case studies are presented based on three varying mission profiles: (a) commercial air carriers, (b) general aviation (GA) aircraft, and (c) helicopters. The findings inform on sUAS-aircraft encounter evolution and trends, including areas of encounter risk, lateral and vertical encounter separation distances, sUAS operator compliance with operational and altitude restrictions, and comparisons of objective detection data against sUAS sighting reports. Recommendations are provided to mitigate risks associated with encounter trends to further enhance safety within the NAS.</div>

Modeling Public Concerns for Unmanned Aerial System Operations in the National Airspace System

The Unites States commercial unmanned aerial system (UAS) market was valued at $99.6 million in 2020 and is projected to reach $3.7 billion by 2030. Applications for these commercial UAS range from risk mitigation and surveillance to package delivery. Coupled with these emerging applications are the unique factors that arise with a commercial UAS’s compact size and low-altitude flights among the civilian populace. In this work, a value model was created to attempt to aid in determining the infringement of a UAS operation configuration on a civilian environment. Value functions were developed in an additive model that measures infringement (a UAS’s encroachment or trespass on a right or privilege of a civilian) based on these attributes. The resulting value model was tested by running 100,000 random simulations. Analysis of the simulation results and sensitivity analysis of the model showed that fleet size, proximity, duration of the operation within a certain proximity, and total operation duration were the attributes that drove a UAS operation’s infringement on an environment. A use case of an Amazon delivery application was then examined. This is a realistic scenario simulated to study if duration of an operation and time near a structure are kept to a minimum, the infringement of an operation remained low. This study provides initial determination of how infringement of UAS can be quantified and what infringement values may look like for different operational scenarios.

Intelligent Spectrum Management for Future Aeronautical Communications

The emergence of new aerial vehicles into the airspace and the continued growth of aviation operations will result in an increased demand for voice and data communications throughout the National Airspace System (NAS). However, recent studies have shown that the continued use of existing aviation spectrum allocations will not meet the anticipated needs of future aerospace applications; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating the use of modern advancements in wireless communications, artificial intelligence (AI), machine learning (ML), and other advanced concepts in order to implement a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace. This technical investigation considers various airspace communications configurations, including air-ground and air-air communications, and can be applied to the existing air traffic control (ATC) system, as well as to future applications, such as the emerging Advanced Air Mobility (AAM) initiative. To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development, which will continue to evolve in support of new and advanced airspace applications. It is anticipated that this proposed spectrum management concept will allow for increased spectrum utilization efficiency and enhanced airspace capacity, which will better serve the needs of future NAS applications.

A Comparison of Airport Risks: Unmanned Aircraft Systems (UAS) Sightings, Wildlife Strikes, and Runway Incursions

To provide a context for the potential threat of unmanned aircraft systems (UAS) sightings on airport operations, this paper compares the characteristics of UAS sightings with two common airport threats: wildlife strikes and runway incursions. This study analyzed over 60,000 events in a three-year period (September 2016 to August 2019), including 6,551 UAS sightings from the Federal Aviation Administration (FAA) UAS Sightings Report database, 47,574 wildlife strikes from the FAA Wildlife Strike database, and 6,041 runway incursions from the FAA Runway Safety database. The results suggest both similarities and differences among the airport threats. Both UAS sightings and wildlife strikes vary by time of year and time of day. UAS sightings and wildlife strikes farther from the airport occur at higher altitudes than sightings and strikes occurring close to the airport. However, UAS sightings are reported at higher altitudes than wildlife strikes, and the distance of UAS sightings from the airport is farther than that of wildlife strikes, in general. The severities of UAS sightings and runway incursions are similar. Pilots take evasive actions in three percent of UAS sightings, and runway incursions of severity A and B are also rare. Pilots of general aviation (GA) aircraft reported the most UAS sightings, and GA operations are also involved in more runway incursions. Considering the kind of airport affected, UAS sightings and wildlife strikes are more common at primary airports, notably large and medium hub airports, whereas runway incursions are more common at reliever airports. Generally, UAS have had a minimal impact on airport operations despite their growing prevalence, which reflects the overall success of integrating this new airspace user into the national airspace system.

Exploring the Use of Geographic Information Systems to Identify Spatial Patterns of Remote UAS Pilots and Possible National Airspace Risk

The proliferation of Unmanned Aircraft Systems (UAS) in the United States National Airspace System (NAS) has resulted in an increasing number of close encounters between manned aircraft and UAS, which correlates with the increasing number of remote pilots in the Federal Aviation Administration (FAA) airmen database. This research explores spatial patterns of registered airmen using Geographic Information Systems (GIS) analyses that provide notable spatial distribution patterns of pilots and how they relate to UAS sightings and airspace categories. The application of GIS to these aviation data may assist safety practitioners with identifying geographic patterns, areas of higher risk, and ultimately improve safety management. The authors analyzed publicly available airmen data to examine spatial distribution patterns, data correlations, and inferences. Airmen addresses were first geocoded into ArcPro 10.4 GIS software as a vector data layer containing attribute values of the database. The spatial analysis tool set was then utilized to establish clustering, density patterns, and spatial relationships between various categories of registered airmen. These density analyses revealed implicitly that commercial registered pilots tend to have the highest clustering near major commercial use controlled airspace, yet registered remote (UAS) pilots are also clustered in these and other densely populated areas. UAS sighting data were also geocoded using zip code values of the reported city to potentially correlate UAS sighting with registered remote pilots, yet the lack of spatial precision in the database made establishing any type of spatial relationship ineffective. The implicit spatial relationships between commercial and remote registered pilots revealed further research is needed to integrate UAS safely and effectively into the national airspace. The poor quality of UAS sighting data also demonstrates the need to better utilize GIS to monitor and track UAS flights within the context of an Unmanned Traffic Management System.

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM