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Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

The identification of precursors to safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach in practice leverages domain expertise to define expected toler...

The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.

En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, we have explored causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). We propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors. In our framework, we first propose an efficient trajectory clustering algorithm to identify nominal routes that reveal both air traffic flow patterns and air route structures in the national airspace system. Second, we develop a tree-based searching algorithm that matches different causal factors to the identified nominal routes in a 4-dimensional manner. Third, we employ a discrete choice modeling framework to quantitatively understand the flight route choice process, and establish a linear model to understand the tactical reroute process. Finally, we estimate the contributions of the identified causal factors to flight inefficiency through counterfactual analysis. Numerical experiments based on 8 representative airport pairs suggest strong negative impact of convection and headwind on route choice process, while MIT, AFP and SAA only demonstrate negative effects on some pairs. We have also shown that the contributions of convective weather, wind, traffic management initiatives, and SAA are up to 7.1%, 14.0%, 4.4%, 21.9%, respectively.

This paper aims to provide a resiliency management strategy of the air transportation network through network stability theory. To ensure the network to recover quickly from an upset condition, an ...

The authors would like to thank the following people for participating in the 2018 UAS Safety Symposium at Georgia Tech since that effort provided much of the driver for this research: Roy Burke, Julianna Burke, Anthony Mormino, Eric Richey, Chris Proudlove, Naira Hovakimyan, Jeong Jur, Greg Ourada, and Michael Miller. The authors are also thankful to Olivia Jagiella-Lodise and Philippe Monmousseau for their contributions in editing and formatting this paper.

Unmanned aircraft systems (UAS) collaborate with humans to operate in diverse, safety-critical applications. However, assurance technologies need to be integrated into the design process in order to guarantee safe behavior, thereby enabling UAS operations in the National Airspace System (NAS). In this paper, formal methods are integrated with learning-enabled systems representations. The generation and representation of knowledge are captured via monadic second-order logic rules in the cognitive architecture Soar. These rules are translated into timed automata, and a proof of correctness for the translation is provided so that safety and liveness properties can be checked in the formal verification environment Uppaal. This approach is agnostic to the learning mechanism used to generate the learned rules (e.g., chunking, etc.). An example of a fault-tolerant, learning-enabled UAS deciding which of four contingency procedures to execute under a lost link scenario while overflying an urban area is used to illustrate the approach.

Commercial space transportation is becoming more affordable and accessible. Consequently, we expect to see significant expansion of commercial space launch activities in the coming decade. As space vehicles travel through airspace during the launch and re-entry stages, they potentially disrupt the regular operations of traditional users. This paper estimates the potential economic and operational impacts of commercial space horizontal launch activities on airlines under various launch scenarios using predictive fast-time simulation modeling, focusing on Cecil Air and Space Port in Jacksonville (Florida) and the rules governing the national airspace system (NAS) in the United States. Our results indicate that the existing 4-hour airspace closure rule impacts a significant number of flights, resulting in flight time delays, additional flight distance and fuel burn, as well as other direct operating costs. Safely reducing the duration of airspace closures could serve as a simple solution to mitigate the impacts on airlines and other traditional NAS users. More importantly, treating our studied launch vehicle as an aircraft and opening its departure/arrival corridor to air traffic during a horizontal launch and return would potentially reduce the impacts on airlines significantly, depending on the location of the spaceport, planned flight paths and the trajectory of the launch.

In the past few years, there has been a growth in Unmanned Aircraft Systems (UAS) numbers in segregated airspace. However, although there is an interest in integrating large UAS in the National Airspace System (NAS), safety challenges regarding this insertion arise from the inclusion of new ways of reaching unsafe states. Although UAS may be used in different situations and brings several advantages to the airspace (e.g., efficiency), it may bring uncertainties due to the lack of familiarity of Air Traffic Controllers (ATCos) in these operations. Furthermore, the Terminal Maneuvering Area (TMA) is a critical control area generally established at the confluence of Air Traffic Service (ATS) routes in which the aircraft tend to be closer to each other. Besides, defining a final arrival segment for a set of aircraft in a complex environment is challenging. Thereupon, the main objective of this research is to propose a parallel swarm-based method for optimizing final aircraft arrival segment design (i.e., routes that connect the final sector to the Initial Approach Fix - IAF) considering the presence of aircraft of multiple Technology Maturity Levels (TML) - including the UAS. This is conducted from two perspectives: ATCo workload (which is related to safety) and sequencing duration (which is related to efficiency). Furthermore, different phases of UAS integration are considered using the Technology Maturity Levels (TMLs). Finally, the solutions consider airspace restrictions (e.g., minimum separation between aircraft and bad weather conditions). The experiments conducted show that this approach can build safe and efficient solutions, even in situations with many aircraft.

Unmanned aircraft systems (UAS) must comply with specific standards to operate in the National Airspace System (NAS). Among the requirements are the detect and avoid (DAA) capabilities, which include display, alerting, and guidance specifications. Previous studies have queried pilots for their subjective feedback of these display elements on earlier systems; the present study sought pilot evaluations with an initial iteration of the unmanned variant of a Next Generation Airborne Collision Avoidance System (ACAS XU). Sixteen participants piloted simulated aircraft with both standalone and integrated DAA displays. Their opinions were gathered using post-block and post-simulation questionnaires as well as guided debriefs. The data showed pilots had better understanding and comfort with the system when using an integrated display. Pilots also rated ACAS XU alerting and guidance as generally acceptable and effective. Implications for further development of ACAS XU and DAA displays are discussed.

Autonomous operation of unmanned aerial vehicles (UAVs) requires development of technologies that allow for safer flight control and response to various flight anomalies. Software for autonomous co...

Recent years have witnessed a booming of the industry of civil Unmanned Aircraft System (UAS). As an emerging industry, the UAS industry has been attracting great attention from governments of all countries and the aviation industry. UAS are highly digitalized, informationized, and intelligent; therefore, their integration into the national airspace system has become an important trend in the development of civil aviation. However, the complexity of UAS operation poses great challenges to the traditional aviation regulatory system and technical means. How to prevent collisions between UASs and between UAS and manned aircraft to achieve safe and efficient operation in the integrated operating airspace has become a common challenge for industry and academia around the world. In recent years, the international community has carried out a great amount of work and experiments in the air traffic management of UAS and some of the key technologies. This paper attempts to make a review of the UAS separation management and key technologies in collision avoidance in the integrated airspace, mainly focusing on the current situation of UAS Traffic Management (UTM), safety separation standards, detection system, collision risk prediction, collision avoidance, safety risk assessment, etc., as well as an analysis of the bottlenecks that the current researches encountered and their development trends, so as to provide some insights and references for further research in this regard. Finally, this paper makes a further summary of some of the research highlights and challenges.

The arrival sequencing and scheduling problem have been formulated in the urban air mobility (UAM) context for homogeneous and mixed fleets of electric vertical takeoff and landing (EVTOL) aircraft...

The continuous dynamics of unmanned aerial vehicles (UAVs) are generally modeled as a set of differential equations. Traditionally, these continuous dynamics of UAVs are analyzed using paper-and-pe...

When the Federal Aviation Administration (FAA) launched the System Wide Information Management (SWIM) initiative, the FAA had the goal of using the same portable, open infrastructure across all participating systems in the National Airspace System (NAS). Around 2008 for SWIM Segment 1, the FAA chose Iona Software's Free/Open Source Software (FOSS) based bundle, which was known and supported under the Fuse brand. The FAA obtained the licenses used by programs, including EnRoute Automation Modernization (ERAM), through Iona, which was later acquired by Progress and RedHat.

Metrics are the key tools to monitor the safety performance of complex systems like air traffic control. Developing risk metrics is a complex process, which involves prediction of risk and identification of different potential outcomes of undesired safety events. For instance, a metric that helps monitor the risk on the surface in an airport environment needs to detect and identify events such as runway excursions, runway incursions, and taxiway incidents and assign appropriate numerical indices proportional to the outcome of each event. An accident with a fatality, injury and aircraft damage will have a corresponding weight for each outcome and incident that involves no outcome can be assigned a severity weight based on its probability of becoming an accident. Models that support such metrics needs to be able to process data from diverse sources. In this paper, we discuss key aspects of two comprehensive metrics that the office of Safety in the Air Traffic Organization has deployed recently: 1) How to employ an automation to detect all relevant events from different data sources and 2) how to assign severity weights for different types of events (accidents and incidents).1. Detection of Relevant Events: one key element of supporting a comprehensive metrics is an automated detection of relevant events by categories. Unlike in aviation, the use of AI and ML have permeated in most industries. Aviation is a safety-critical domain in which there is very little tolerance for failures. The stringent requirements of aviation have contributed to the slow adoption of AI and ML in aviation, in general. However, many aviation sectors are increasingly adopting AI systems, ranging from automating simple, yet tedious and repetitive tasks to a more sophisticated application of a complex autonomous air traffic control system to de-conflict traffic. In this paper, we outline how machine learning models were employed to identify relevant accidents and incidents to support two metrics, a surface safety metric and an airborne safety metric.2. Severity Weighting Scheme: for a metric to be a comprehensive measure and indicate the overall performance of the system, it needs to account for various types of accidents and incidents (precursors) that occur in the system. This paper shows how a weighting scheme we developed to measure the outcome of accidents, such as injuries to people and damage to property, as well as probabilistically determine the severity of incidents with no outcome. The aggregation of all weights along with the frequency of occurrence in a given period represent the overall safety performance of the system.

Trust is a key issue in the development of autonomous systems. Humans and autonomous function or systems should be able to establish trust in each other’s actions. With advancing technology, innovations, and growth in Unmanned Aircraft System (UAS), ensuring trust in the use of automation and autonomy poses a major challenge. It is important that the automation in UAS operations is at an acceptable level of reliability, integrity, and redundancy. The bigger challenge is to approve the automation on the UAS and ground stations, which requires consideration of the complexity of operation, how automation is used in the operation, widely differing types of UAS, and the lack of standards to certify the automated elements. There is a need to examine relevant operational, technical, and procedural implications of automation in addition to analyzing system failures, contingent backup options in place for unforeseen circumstances, the role of the human, and training needs for the pilot or operator. It is important to account for these factors in defining standards and certification methods.With new concepts and technologies, and the tremendous emphasis on autonomy and automation, we assert a new method to prove the automation and transition plans for the required confidence level in automation. The method features a framework that accounts for new operations and interactions between all systems and stakeholders in the National Airspace System (NAS). Decision makers at all levels will find accelerated processing opportunities when using this framework to refine standards criteria, assess automation maturity, and focusing solution discussions with key stakeholder groups.The paper will describe the approach to classification of the automation role and human role leading to insights into the acceptable paths forward for automation inclusion. The strategic approach will enable the stakeholders to see the impact of UAS operating in widely varying environments, while ensuring operations remain safe and secure. The development of this comprehensive list with identified roles for automation and humans, as well as the interactions between humans and automation functions, will help with better planning, risk assessment, regulations, and policies as well as other integration activities.

Dynamic Airspace Configuration (DAC) has caught attention in tackling the increasing demand for flight operation in the National Airspace System (NAS). In recent years, sectorisation methods, such as the Voronoi diagram, dynamic programming, genetic algorithm, have been developed to find the optimal sector design. However, by restructuring the airspace, most design methods reduce the airline’s delay but increase the complexity of airspace configuration, which causes uncertainties to safe air traffic management. Furthermore, adaptive airspace reconfiguration schemes are also desired to accommodate the designs of different types of airspace. This paper presents a study exploring the concept of time-variant stability of airspace configuration transition in response to the assumed flight fluctuation. We exploited the airspace capacity in explaining the potential long-term effects caused by the graph-based airspace partitioning algorithm. According to the imbalanced distribution of air traffic, the aircraft counts of each divided sector are computed through time to estimate the workload of air traffic controllers (ATC).The number of flights in the sub-sector is computed through two months’ time length. Airspace designs from the Voronoi diagram are presented and evaluated by the airspace capacity of the ZJX ARTCC sector. Two configuration scenarios are chosen to present the algorithms’ evolving performance. The simulation results show that the existing graph partitioning method is not adapted to imbalanced traffic patterns, which does not show the potential of the reconfiguration algorithm’s robustness in response to the fluctuation of air traffic. As part of the necessary analysis we developed, we concluded the airspace sectorization for imbalanced traffic patterns and complex airspace characteristics, which provides the transferable features of airspace design between different ARTCC sectors.

Quantitative analysis is required as part of managing the automation systems which provide Air Traffic Management (ATM) services for the National Airspace System (NAS). The Federal Aviation Administration (FAA) invests significant levels of funding for such analyses for both post-hoc evaluation of tools/techniques, as well as prospective evaluation of proposed new capabilities. Statistical analysis is one quantitative analysis technique to assess the impact of a newly implemented automation capability. The general approach is to compare air traffic operations performance metrics (e.g., flight time/distance) before and after the activation of a new capability.

The main goal of this research is to evaluate the Air Traffic Controller (ATCo) workload considering the Unmanned Aircraft System (UAS) integration into the National Airspace System (NAS) through fast-time simulations, and including futuristic scenarios in which the ATCo is familiar with manned and unmanned aircraft, i.e., different ATCo mindsets are considered. As these professionals play an essential role in optimizing the airspace operation, maintaining their workload at an acceptable level is essential. However, the integration of new technologies, such as UAS, may present an impact on safety levels from the workload perspective. In this context, the Technology Maturity Level (TML), which is a systematic metric/measurement system that supports assessments of the familiarity of a particular aircraft with ATCos, is proposed. The experiments showed that the integration of UAS into the NAS should be conducted gradually.

Emergency landing is a critical safety feature for the expected increase of autonomous vehicles in the airspace. This paper presents Safe2Ditch, an autonomous crash management system for landing sm...

As air traffic management becomes increasingly automated, technologies for detecting and resolving conflicts are critical to maintain the safety of an airspace. There exists a need to understand ho...

By modeling compatibilities, benefits, and penalties of various technology packages at the subsystem level in a vehicle design tool, bottom-up assessments of projected technological benefits can be...

The last few years have seen an increased interest in deep learning (DL) due to its success in applications such as computer vision, natural language processing (NLP), and self-driving cars. Inspired by this success, this paper applied DL to predict flight demand and delays, which have been a concern for airlines and the other stakeholders in the National Airspace System (NAS). Demand and delay prediction can be formulated as a supervised learning problem, where, given an understanding of past historical demand and delays, a deep learning network can examine sequences of historic data to predict current and future sequences. With that in mind, we applied a well-known DL method, sequence to sequence (seq2seq), to solve the problem. Our results show that the seq2seq method can reduce demand prediction mean squared error (MSE) by 50%, compared to two classical baseline algorithms.

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostly rely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-recurrent design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicate the effectiveness of Long-Short Term Memory (LSTM) and GRU cells for state-of-the-art data processing (interpolation). Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value by improving predictions along a flight altitude; however, tailored products such as CIWS Echo Top remain the most holistically accurate.

The role of the en route air traffic control specialist (ATCS) is vital to maintaining safety and efficiency within the National Airspace System (NAS). ATCSs must vigilantly scan the airspace under their control and adjacent airspaces using an En Route Automation Modernization (ERAM) radar display. The intent of this research is to provide an understanding of the expert controller visual search and aircraft conflict mitigation strategies that could be used as scaffolding methods during ATCS training. Interviews and experiments were conducted to elicit visual scanning and conflict mitigation strategies from the retired controllers who were employed as air traffic control instructors. The interview results were characterized and classified using various heuristics. In particular, representative visual scanpaths were identified, which accord with the interview results of the visual search strategies. The highlights of our findings include: (1) participants used systematic search patterns, such as circular, spiral, linear or quadrant-based, to extract operation-relevant information, (2) participants applied an information hierarchy when aircraft information was cognitively processed (altitude -&gt, direction -&gt, speed), (3) altitude or direction changes were generally preferred over speed changes when imminent potential conflicts were mitigated. Potential applications exist in the implementation of the findings into the training curriculum of candidates.

Current operations in the National Airspace System (NAS) primarily rely on voice communication designed to minimize lengthy conversations as well as simple data exchanges intended to supplement voice communication without adding additional fidelity to instructions. Advances in communication, data processing capabilities, and cultural acceptance of technology-enabled procedural changes are paving the way for implementation of Air Traffic Management (ATM) concepts based on Four-Dimensional Trajectories (4DT). Exchanging Four-Dimensional (4D) data (latitude, longitude, altitude, and time) and trajectories via an internet- based infrastructure is part of the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) Office’s vision for Trajectory Based Operations (TBO). In 2020, the FAA successfully completed the 4DT Live Flight Demonstration (4DT LFD) project to demonstrate how 4DT-based operations in the NAS can be implemented with current technologies and to inform future research.The FAA partnered with key industry participants to conduct two live flights in the NAS using 4DT for controller-pilot negotiations, clearances, and aircraft intent information. The 4DT LFD leveraged a connected aircraft with an Internet Protocol (IP) data link that enabled Air-to-Ground (A/G) SWIM and Demo IP-based Clearance connectivity as the aircraft traversed the NAS. The project maximized use of current infrastructure while leveraging NextGen capabilities at the FAA’s Florida NextGen Test Bed (FTB) and communication systems available from industry partners.The 4DT LFD elaborated procedures, validated existing technology, and began to socialize the feasibility of TBO in the NAS to ATM stakeholders including the FAA, airspace users, and system providers. The project focused on new data exchange mechanisms for trajectory negotiations in the NAS enroute environment, leveraging commonly used avionics equipment with emphasis on Electronic Flight Bag (EFB) and Flight Management System (FMS). The project also leveraged ongoing FAA NextGen investments, such as System Wide Information Management (SWIM), Performance Based Navigation (PBN), and research into Flight and Flow – Information for a Collaborative Environment (FF-ICE). Lessons learned from the project will inform future FAA system requirements and airspace users’ development of their own procedures and technologies. These lessons will help refine global concepts, standards, recommended practices, and implementation guidance.

As the Federal Aviation Administration (FAA) moves to a Global Navigation Satellite System (GNSS)-centric navigation strategy, the agency has not refreshed their legacy Ground-Based Navigation Aids (NAVAIDS). These NAVAIDS include Instrument Landing Systems (ILS) and Distance Measuring Equipment (DME) providing precision all-weather landing services, Very High Frequency Omni-directional Range (VOR) and DMEs providing traditional en route airways and Standard Instrument Departure/Arrival Route (SIDs/STARs) procedures, as well as DMEs operating in DME-DME networks supporting NextGen Performance Based Navigation (PBN). The legacy NAVAID fleet provides a vital role in providing GNSS resilient navigation services to Civil and Military users within the National Airspace System (NAS). The FAA faces continued supportability issues and declining availability due to the age of the legacy NAVAID fleet, directly impacting the ability of the FAA to provide navigation services in the event of a major disruption to GNSS services. A major obstacle in refreshing the NAVAID fleet is the cost of qualification and implementation of new equipment for operation within the NAS. The FAA can greatly accelerate the time to implement while simultaneously reducing the acquisition costs by using Commercial Off-The-Shelf (COTS) or Non-Developmental Item (NDI) acquisition strategies. With a COTS based acquisition strategy, the Government would seek to minimize or eliminate custom engineering, eliminate creation of Government specific documentation including manuals and training materials, and maximize use of Original Equipment Manufacturer (OEM) design documentation, safety analysis, and logistics processes and procedures. These strategies have been successfully employed by the majority of Air Navigation Service Providers (ANSPs) to reduce costs without compromising safety or quality.

As the FAA progresses toward full implementation of the Next Generation Air Transportation System (NextGen) and continues to chart the architecture of the National Airspace System (NAS) into the future, investigating the feasibility of deploying NAS services into a Cloud environment is one potential opportunity to modernize the current technological systems of the NAS. General benefits of Cloud migration include the consolidation and outsourcing of infrastructure acquisition and management; transparent and evolutionary technology refresh; and elasticity of computing, storage, and other infrastructure requirements to reduce operational costs and increase business agility. In addition to these benefits, migration to the Cloud also enhances the opportunity for common use of services between NAS systems.

With the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations, there will be an increasing demand for voice and data communications within the National Airspace System (NAS). The continued use of existing VHF and UHF frequency allocations is not a sustainable approach, and as a result, a new spectrum management solution is required to support future mission needs. The proposed spectrum management concepts leverage modern advancements such as artificial intelligence (AI) and big data to dynamically optimize the spectrum utilization based on the predicted communications demand throughout the airspace. This technical investigation considers both air-ground and air-air communications networks, and can be applied to both existing applications such as the air traffic control (ATC) system, as well as future applications, such as the emerging Advanced Air Mobility (AAM). To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development and will continue to evolve to support new and advanced airspace applications. It is anticipated that this proposed spectrum concept will better serve the spectrum needs of future NAS applications.

To perform Trajectory Based Operations (TBO) effectively and efficiently in the future, extensive collaboration between Airspace Users (AU) and ATM Service Providers (ASPs) will be necessary. The International Civil Aviation Organization’s (ICAO) Flight & Flow Information for a Collaborative Environment (FF-ICE) provides a framework for that collaboration to occur with the aim of overcoming the limitations of today’s flight planning as well as to provide flexibility for a future where users share information in real time, leading to better strategic planning and operational efficiency. The ICAO Air Traffic Management Requirements and Performance Panel (ATMRPP) developed the Manual on FF-ICE and released it as ICAO Document 9965. Subsequently it developed the Provisions, Implementation Guidance, and amendments for the first step of FF-ICE, called FF-ICE Planning (FF-ICE/R1), which focused on increased interoperability, efficiency, and capacity in the pre-departure phase of flight using mostly automated ground-ground exchanges. The ATMRPP is further developing the FF-ICE concept to include the FF-ICE during post-departure execution (FF-ICE/R2). FF-ICE/R2 development focuses on Strategic collaboration and the transitions between Strategic and Tactical.To prepare the National Airspace System (NAS) and the global aviation community for the adoption of FF-ICE/R1, the Federal Aviation Administration (FAA) successfully validated FF-ICE/R1 Provisions and Implementation Guidance both within a single and multiple Enhanced ASPs (eASP) in a 2018 project named International Interoperability Harmonization and Validation (IIHV while the second demonstration incorporated international and oceanic environments involving multiple eASPs, including strategic boundary coordination between FAA and both Japan Civil Aviation Bureau (JCAB) and NAV CANADA.The FF-ICE/X demonstration project matured operational concepts and technical approaches to FF-ICE/R2. FF-ICE/X also documented findings related to using FF-ICE/R2 inside the NAS and across multiple eASPs, including how FF-ICE affects boundary coordination.

The next generation of aviation will operate in a data-driven environment. Aviation’s destiny is now coupled with the Internet of Things (IoT)- wherein devices and autonomous systems have an inherent need for continuous network connectivity. For next generation technology to reach its full potential, a network connection should be available anywhere, anytime. Unmanned Aerial Vehicles (UAVs or drones) are increasing in number and large companies are designing drone delivery systems. A next generation Unmanned Aerial System (UAS) will require a more flexible network that can deliver data in non-traditional environments. However, looking beyond UAS flight operations, the aviation industry at large needs to consider integrating new services into the Air Navigation Service Provider’s (ANSP’s) enterprise communications architectures-both to get low-cost communication to remote areas for UAS operations and as a cost-effective last mile alternative for high-availability, low latency services.These developments will require a communication network that is scalable to the needs of the future. Low and Medium Earth Orbit (LEO/MEO) satellite constellations can address the challenges of a rapidly evolving future by being able to provide communications beyond the existing commercial and public infrastructure, to include a global broadband, low-latency footprint. Remote regions typically require costly site-specific infrastructure solutions to provide last-mile connectivity. By utilizing lower orbits, the constellation can provide high data rates and low latency on the links by bringing the edge of broadcasting closer to the Earth versus a Geosynchronous Orbit (GEO). The telecommunications industry is at a crossroads for determining the network design for tomorrow’s aviation requirements. The introduction of UAVs into the National Airspace System (NAS) will present the challenge of locating and surveilling these small and elusive aircraft. The time is now to build a new infrastructure; a Satellite Communication (SatCom) network, that achieves rapid global broadband coverage, versus localized terrestrial networks.

The Federal Aviation Administration’s Office of NextGen Division of Systems Analysis and Modeling estimates the long-term operational impacts and future benefits of planned National Airspace System (NAS) initiatives, including the impact of emerging new systems in the NAS. To estimate future benefits, we use a fast-time simulation model of the entire NAS with future projected flight schedules. In order to model the effects of emerging Unmanned Aircraft System (UAS) operations in our simulation, we create hypothetical UAS flight plans based on historical UAS flights. Our fast-time simulation requires flight plans with entire 4-D trajectories, origin and destination airports, wheels-off and wheels-on times, a sequence of latitude and longitude pairs specifying the route of flight, and aircraft performance characteristics. To develop these flight plans, we identified UAS traffic from historical radar data by call sign, aircraft type, and other indicators of UAS flights. Then we filtered the identified UAS traffic to select only those that entered controlled airspace. This briefing will present an overview of recent trends in UAS operations in controlled airspace, including trends in operation counts, geographical locale, and altitude band. Finally, we present findings from initial simulation runs used to estimate the impact of historical UAS on traditional traffic in controlled airspace.

Risk of runway excursion caused by pilots continuing an unstable approach to landing has been identified by aviation accident investigators as a primary contributing factor in airline landing accidents. The purpose of this research was to develop and test predictive models for Unstable Approach Risk Misperception (UARM) in the National Airspace System using machine learning. The research applied machine learning algorithms to flight recorder data gathered from a fleet of commercial transport aircraft and made available by the National Aeronautics and Space Administration [1]. Once evidence of unstable approaches was identified and extracted from the flight recorder data, a determination was made whether a rejected landing or continuance to landing was made. Federal Aviation Administration unstable approach criteria were used in the identification of unstable approaches based on flight data variables evaluated at 500 feet above the ground on approach to landing [2]. Six machine learning algorithms were used, including logistic regression, decision tree, neural network, gradient boosting, random forest, and support vector machine. The results indicated the decision tree with three branches produced the best predictive model. This model was able to predict the pilot error of continuing an unstable approach to landing with an accuracy of 98 percent. Glideslope, approach airspeed, localizer displacement, and flap setting were the most important influential predictors to the pilot error incidents.

The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For many aviation safety studies, manned aircraft behavior is represented using dynamic Bayesian networks. The original statistical models were developed from 2008-2013 to support safety simulations for altitudes above 500 feet Above Ground Level (AGL). However, these models were not sufficient to assess the safety of smaller UAS operations below 500 feet AGL. In response, newer models with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different than models of fixed-wing aircraft to require type specific models. The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low altitude operations. We also address which models can be surrogates for noncooperative aircraft without transponders., 8 pages, 2 figures, 2 tables, 3995 total words

Unmanned Aircraft Systems (UAS) need to be able to comply with the Code of Federal Regulations Part 91 ‘see and avoid’ requirement. Detect and avoid (DAA) is an essential enabler for integrating UAS into the National Airspace System. A DAA system is a suite of sensors, trackers, and alerting and guidance algorithms that assists a remote pilot in maintaining separation from airborne traffic. Previous works have focused on operations transiting to and from Class A or special use airspace. More recent efforts have defined DAA system requirements for operations in and around the airport terminal environment. In continuation of this effort, this paper defines the DAA Terminal Area (DTA) size and switching technique for the DAA Well Clear (DWC) volume while transitioning between en route and the terminal area.

Over the past decade, the proliferation of Performance Based Navigation (PBN)-enabled diverging departure procedures has increased departure capacity, reduced delay, and lowered aircraft emissions at major airports across the National Airspace System. However, increasing departure capacity at airports with narrow departure corridors remains challenging as implementations of additional departure paths and the current requirement for immediate divergence may markedly change an airport's noise footprint. This paper explores options for capacity-enhancing application of divergence-based initial spacing without requiring aircraft to diverge immediately after departure. It presents the analysis approach taken to characterize departure performance of aircraft observed in actual operations and the simulation model developed to estimate collision risk. The results indicate that the proposed operations meet target level of safety requirements for aircraft on PBN procedures that diverge within 10 miles after departure and suggest the feasibility of realizing departure capacity benefits while minimizing changes to the noise footprint of the operations.

Detect and avoid (DAA) systems provide unmanned aircraft systems (UAS) with an alternative means of compliance with the see-and-avoid requirements associated with operations in the National Airspace System (NAS). Previous studies have examined the efficacy of different DAA alerting and guidance structures and formats. Prior research has also investigated the integration of DAA information with the alerting and guidance generated by the Traffic Alert and Collision Avoidance System (TCAS II). The next-generation replacement for TCAS II – the Airborne Collision Avoidance System X (ACAS X) – includes a variant to be used by UAS (ACAS Xu) that will provide both DAA and Collision Avoidance (CA) guidance. The alerting and guidance issued by ACAS Xu differs from previous DAA and CA systems, a result of new capabilities that were not available to earlier systems. Differences include the removal of warning-level DAA alerting and guidance, as well as the issuance of new types of CA guidance, referred to as Resolution Advisories (RAs). Whereas TCAS II only issues vertical RAs, ACAS Xu adds horizontal and blended (i.e., simultaneous vertical and horizontal) RAs. The current study assessed pilots' ability to respond to and comply with the DAA and RA alerting and guidance generated by ACAS Xu in a human-in-the-loop simulation. Sixteen active UAS pilots participated in the study and were tasked with responding to scripted DAA and RA traffic conflicts. Results showed that pilots were effective at making timely maneuvers against DAA threats. The proportion of losses of DAA well clear against noncooperative intruders was found to be significantly higher than the proportion of losses against cooperative intruders, a result of the limited declaration range of the simulated onboard RADAR. Results also demonstrated that pilots could consistently meet the five second response time requirement for initial RAs. Rapid responses to RAs had the corresponding effect of minimizing the severity of losses of DAA well clear. While pilots complied with initial RAs at a high rate, compliance dropped substantially when the target heading was updated during a horizontal RA. Pilot performance with ACAS Xu will be presented alongside results from prior DAA research.

Interval Management (IM) is an FAA Next-Gen Automatic Dependent Surveillance – Broadcast (ADS-B) In application designed to decrease the variability in spacing between aircraft, thereby increasing the efficiency of the National Airspace System (NAS). One application within IM is Paired Approach (PA). In a PA operation, the lead aircraft and trail aircraft are both established on final approach to dependent parallel runways with runway centerline spacing less than 2500 feet. The trail aircraft follows speed guidance from the IM Avionics to achieve and maintain a desired spacing behind the lead aircraft. PA operations are expected to require a new separation standard that allows the aircraft to be spaced more closely than current dependent parallel separation standards. The behavior of an airborne collision avoidance system, such as TCAS II or ACAS Xa, must be considered during a new operation such as PA, because the aircraft are so closely spaced. This analysis quantified TAs and RAs using TCAS II Change 7.1 and ACAS Xa software with simulated IM PA operations. The results show no RAs using either TCAS II Change 7.1 or ACAS Xa, negligible TAs using TCAS II Change 7.1, and acceptable numbers of TAs using ACAS Xa software during simulated PA operations.

Today's National Airspace System (NAS) Traffic Flow Management (TFM) collaboration tools developed for the Federal Aviation Administration (FAA) are not sufficient to meet the trajectory based operations (TBO) needs of the future, namely, real time data sharing, user collaboration, and improved strategic traffic management planning. To more efficiently and effectively collaborate among NAS stakeholders and across all TFM automation systems, The MITRE Corporation (MITRE)'s Center for Advanced Aviation System Development (CAASD) is researching a common dashboard to facilitate more complex TFM collaborative decision-making needs. A common visualization dashboard will be dynamic and data-driven to provide detailed information and meet the identified collaboration requirements so that stakeholders can work together in real-time across multiple facilities to formulate solutions to complex, multi-faceted problems. To that end, we have researched existing methods for improving collaboration and identified the principles and best practices of collaborative dashboard design to apply to NAS TFM scenarios. This research helps inform a future mode of operation that focuses on the collaborative process between FAA and flight operators, instead of simply providing situation awareness via monitoring and alerting. A common web-based platform to support decision making and record actions will enable more effective collaboration among TFM stakeholders. We analyzed the collaboration between the FAA and flight operators for an example scenario, as a first step toward defining the features needed to facilitate those interactions. To support collaborative decision-making, we envision a composable frontend for a NAS TFM problem-specific view or workspace.

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA's UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

This paper discusses the use of an on-board radar altimeter and Digital Elevation Model (DEM) for terrain referenced integrity monitoring during an Unmanned Aircraft Systems precision approach and landing. For operation of commercial UAS in the National Airspace System (NAS), a Performance Based Navigation (PBN) system applicable to all phases of flight is needed for unrestricted airspace and airport access. Methods, such as Radar Altimeter (RALT) Aiding, are currently being researched to create a Global Navigation Satellite System (GNSS) based navigation system with sufficient positional accuracy for a CAT-IIIc like precision approach and landing. While these systems have successfully addressed the vertical accuracy, horizontal accuracy, and vertical integrity requirements, further development for horizontal integrity is needed. This paper builds on previous work by the authors on radar altimeter augmentation to GNSS and discusses additional measures to improve the horizontal integrity of the navigation system. Performance of the proposed system is assessed in nominal and off-nominal conditions using data collected with a DC-3 aircraft at the Ohio University airport.

As the aviation community integrates Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS), one of the technical issues that must be handled concerns the Command and Control (C2) Link from the remote pilot Control Station (CS) to the UAS. The C2 Link can switch between carriers during the UAS mission. For example, the link may switch from a terrestrial network to a satellite-based communications network (SATCOM), or from one terrestrial network to another. Requirements for interworking and security of the C2 Link switchover between two point-to-point Line of Sight (LOS) links, switchover between LOS terrestrial C2 Link and a SATCOM network, and switchover between Radio Line of Sight (RLOS) terrestrial C2 Link and a commercial terrestrial cellular network have been developed, and this paper discusses the validation of those requirements.

In autonomous air-traffic management scenarios of the future, manned and unmanned aircraft will be able to safely navigate through the National Airspace System, independent of centralized air-traffic controllers. They will do this by sharing critical data necessary for maintaining standard separation with each other. Under such conditions, every aircraft must have sufficient knowledge about other aircraft sharing the airspace to operate safely. In this paper, we specify a safe state of knowledge that is necessary for aircraft to operate safely in the absence of a centralized air-traffic controller and present a distributed knowledge propagation protocol to attain this safe state. This protocol can be used by network-connected aircraft to achieve collaborative situational awareness for cooperative flight planning. We identify certain system conditions necessary to guarantee two correctness properties for our protocol – safety and progress. We use the TLA + Specification Language to formally specify our protocol, the correctness properties, and the conditions necessary to guarantee the properties. Using the formal specifications, we also provide mechanically-verified proofs of the correctness properties in the TLA + Proof System.

With the growth of aviation operations and the emergence of new entrants into the National Airspace System, there has been an increasing demand for aeronautical communications. However, the existing VHF/UHF frequency allocations for NAS operations have become increasingly scarce and unable to support the projected growth in demand. While traditional approaches to address the spectrum scarcity issue (such as reduction of channel bandwidth) have provided some relief, there is a need for a more scalable and sustainable solution. Consequently, NASA is investigating a new approach to spectrum management within the NAS using Artificial Intelligence and other advanced technologies. The concept considers both air-ground and air-air communication networks with the anticipated outcome that the proposed concept will meet the spectrum needs of future NAS communications.

NASA is conducting the Airspace Technology Demonstration-2 to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system. The IADS system is powered by real-time System Wide Information Management feeds which provide an accurate and high fidelity view of the lifecycle of a flight. This data can be leveraged to drive efficiencies in the National Airspace System. For non safety critical applications there is opportunity for third party service providers to offer this type of data-driven prediction service in near real-time. This paper investigates the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach. We quantify our predictive accuracy and identify paths for improvement. Through this iterative process we hope to evolve our models and methods to a near real-time prediction service.