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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.

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.

The primary mission of the Federal Aviation Administration (FAA) is to maintain the safety of the National Airspace System (NAS). As part of this mission, the FAA is tasked with ensuring that future air traffic controllers are adequately trained to perform the high-risk job of directing air traffic. The FAA Academy curriculum for newly hired controllers involves 3-4 months of intensive lessons and performance assessments. It has been suggested that this training program is quite stressful, and successful trainees tend to be those who can better manage stress. To support ATC trainees, researchers at the Civil Aerospace Medical Institute (CAMI) have conducted operational research to develop and evaluate a stress management training to help trainees manage their stress during training at the FAA Academy.

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.

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 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.

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.

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

Accurate estimation of airspace capacity is essential to a safe, efficient and predictable air transportation system. Conventional approaches focus on controller workload using airspace complexity measurements that only consider operational conditions of controllers. However, such model-driven methods don’t completely demonstrate airspace capacity in the real world because of lack of consideration for other critical factors such as weather. To address this challenge, we propose a new airspace capacity estimation model based on decision tree ensembles. Our model combines multi-source data to quantify the maximum transportation capacity of en route sector under different circumstances.This paper makes the following contributions: (a) we present an interpretable data-driven model that estimates the capacities of the National Airspace System (NAS), and highlight factor importance for airspace capacities; (b) the airspace capacity estimated by our proposed model is dynamically adjusted based on the real-time environment that has the potential to be a guide for temporary flight path changes or air traffic selections for an emergency landing; and (c) we promote the role of machine learning-based methods in future ATM and airspace optimization.

The United States has the largest, busiest, most complex airspace system in the world. Operating the National Airspace System (NAS) requires a vast and unique infrastructure framework that includes facilities; network communications; terrestrial and space-based navigation; and surveillance equipment. This infrastructure is located throughout the United States for the Federal Aviation Association (FAA) to manage air traffic through all phases of flight. These resources help ensure that Air Traffic Management (ATM) service to the public continues 365 days a year, 24 hours a day, seven days a week, under a wide-ranging set of circumstances, including unplanned system failures.

Over the past two years we have worked on a project for NASA’s Aeronautics Research Mission Directorate (ARMD) University Leadership Initiative (ULI) program. Our project is entitled Hyper-Spectral Communications, Networking and ATM as Foundation for Safe and Efficient Future Flight: Transcending Aviation Operational Limitations with Diverse and Secure Multi-Band, Multi-Mode, and mmWave Wireless links. For brevity we abbreviate this title HSCNA. The four-institution HSCNA project is the only ULI program to address communications and networking, and thus far has been extremely productive: we have published 10 journal papers, 54 conference papers, 2 book chapters, and multiple technical reports, with another 10-20 papers in review, and 2 patent applications. In addition to publications we are developing a dual-band radio system for flight testing in the 2020 Boeing Eco-Demonstrator program, have developed systems for assessing wideband short-range millimeter wave (mmWave) airport radio links, and systems for detection of unauthorized unmanned aircraft systems (UAS). We have also developed a future Concept of Operations (ConOps) document and are developing a simulation tool to assess gains of our HSCNA technologies when used in the National Airspace System (NAS). In this paper we summarize our project and provide example results and findings. We first provide a short overview of the ULI program and its goals within the ARMD Strategic Implementation Plan. We then describe our project’s six primary tasks, which are specifically, (i) the ConOps development; (ii) a comprehensive categorization and evaluation of current and planned communications technologies that can be used for aviation, across frequency spectrum spanning five orders of magnitude (e.g., 3 MHz HF through 100 GHz), including evaluation of performance gaps; (iii) design, development, and proof-of-concept testing of a multi-band aviation communication system; (iv) evaluation of mmWave frequency bands and technologies for use in advanced airport communication applications; (v) evaluation of RF detection of unauthorized UAS via several techniques; and, (vi) development of a simulation system to enable exploration of potential gains of these HSCNA technologies in ATM. The example results we provide include analyses, computer simulations, laboratory experiments, and field testing. We also describe plans for the final phase of our project, and discuss impacts and future work.

The United States National Airspace System is currently operating at a level close to its maximum potential. The workload on the system, however, is only going to increase with the influx of unmanned aerial vehicles and soon, commercial space transportation systems. The traffic flow management is currently managed based on the flight path requests by the airline operators; while the minimum separation assurance between flights is handled strategically by air traffic control personnel. A more tactical approach would be to plan for a longer time horizon which is non-trivial given the uncertainties in the airspace due to weather. In this work, we consider a simplified model of the airspace as a grid of sectors and the uncertainties in the airspace are modeled as blocked sectors. In the modeled airspace with uncertainties, we schedule multiple flights using a dynamic shortest path algorithm. A novel cost function based on potential energy fields is proposed for use in the path planning algorithm to handle blocked sectors. A priority-based contention resolution scheme is proposed to extend the solution to multiple flights. We then demonstrate the proposed framework using a simulated test case.

Flight delays in the U. S. National Airspace System (NAS) present a fundamental challenge to capacity growth under ever-increasing traffic volumes, and lead to significant financial burdens that reverberate across a multitude of aviation industry stakeholders. Roughly 20% of passengers’ total travel time is due to such delays, causing $35 billion annually in lost revenue and impacting not only the airline industry, but the retail, lodging, restaurant, and tourism industries, as well. The Federal Aviation Administration’s effort in aiding decision-making at airports is readily apparent in the Next Generation Air Traffic Control (NextGen) System’s System-Wide Information Management (SWIM) program, and in-flight delay information from the FAA Air Traffic Control System Command Center (ATCSCC). Academic researchers are concurrently developing various algorithms to predict flight delays that include advanced statistics, machine learning, and graph theory using various network topologies. Other stakeholders have initiated delay prediction methods to adjust their operational schedules. This suggests an opportunity to centralize, validate, and integrate the various delay prediction methods under development; furthermore, these methods are limited in scope with regard to geography, operators, and efficacy.The authors propose here a platform supporting the FAA’s Collaborative Decision-Making (CDM) process with the intent of reducing flight delays in the NAS. Building upon existing deep learning algorithms and utilizing the NextGen SWIM program, this research suggests a central delay prediction platform suited to the complex and dynamic needs of America’s airport infrastructure. assessments of risks and sustainability of the proposed platform are presented. The authors interviewed experts in industry and academic fields related to aviation and information technology, and used the information obtained to refine the model. It is anticipated that this model will accurately produce location-specific departure and arrival delay forecasts that can further be integrated into the CDM and Ground Delay Program (GDP) initiatives.

Automatic Dependent Surveillance-Broadcast (ADS-B) has become one of the key technologies in modern-izing the National Airspace System (NAS) by enhancing air traffic surveillance and improving situational awareness. In this study, we used the Global Oceanic Model - a fast-time computer simulation tool- to evaluate the potential workload of air traffic controllers (ATC) using satellite-based ADS-B and reduced separation standards in the North Atlantic (NAT) oceanic airspace. This study uses two metrics to quantify the potential ATC workload. First, the number of tactical resolution maneuvers (changing flight levels, changing Mach number and lateral deviations) used by ATC to resolve potential conflicts. Second, the number of events that ATC need to monitor when aircraft pairs are located within a protection boundary of 50% above the separation minima. Our results show that equipping aircraft with ADS-B technologies can reduce the ATC workload and increase the efficiency and capacity in the NAT airspace.

Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. Dur...

Weather is by far the most important factor in air traffic delays in the United States’ National Airspace System (NAS) according to the Federal Aviation Administration (FAA). Geostationary satellites have been an effective tool for the monitoring of meteorological conditions that affect aviation operations since the launch of the first Synchronous Meteorological Satellite (SMS) in the United States in 1974. This paper will review the global use of geostationary satellites in support of aviation weather since their inception, with an emphasis on the latest generation of satellites, such as Geostationary Operational Environmental Satellite (GOES)-R (16) with its Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM). Specific applications discussed in this paper include monitoring of convective storms and their associated hazards, fog and low stratus, turbulence, volcanic hazards, and aircraft icing.

An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Aggregate rou...

Integration of new flight operations, such as urban air mobility vehicles and commercial space flights, into the National Airspace System (NAS) will require accommodation of new vehicles with different performance and mission profiles into an established traditional commercial and general aviation aircraft operational framework. For example, in this increasingly diverse future air traffic operations, the interaction and integration of on-demand operations with traditional, largely scheduled operations will lead them to share existing commercial airspace with increased requirements on data exchange and control schemes to ensure adequate safety margins. In this new paradigm, the traditional operations will need to transform from current vector-based operation to a more integrated trajectory-based operation (TBO) in which the aircraft's trajectory intents are more strategically planned, precisely tracked, and collaboratively coordinated. Over the years, NASA has been developing, demonstrating and transferring air traffic management concepts to the FAA to support surface, departure and arrival metering, as well as dynamic reroutes for weather avoidance. This paper describes a framework for combining and advancing those NASA efforts to improve arrival and departure demand management by integrating select NASA TBO capabilities with the Traffic Flow Management System (TFMS), Time-Based Flow Management (TBFM), and the Terminal Flight Data Manager (TFDM) tools to enable TBO across the NAS. The overall concept incorporates operator priorities and preferences in a service-oriented approach to managing complex, high demand airports.

The current system used by the FAA to schedule arrivals is the Traffic Based Flow Manager (TBFM). It is a centralized system that gives an operator (airline) no influence over scheduled times of arrival assigned to its flights. Future systems for managing arrival scheduling are proposed as distributed systems. Such a system is called upon to give operators influence to schedule and negotiate resources for their flights, and to resolve other technical challenges, such as eliminating a single point of failure. A distributed system for managing diverse air traffic will need the capability of computing a schedule for the given arriving flights in a way that complies with the operational constraints. This paper contributes an algorithm that computes such a schedule. Although developed as part of an effort toward a distributed system, the algorithm itself is neither inherently distributed nor inherently centralized and can be used in either type of system.

Demand for small unmanned aircraft systems (sUAS) continues to increase in diversity and volume, however applications are currently limited by regulatory requirements for visual observation or special use waivers. For full integration of autonomous sUAS in the national airspace system, a collision avoidance system must be implemented to enable detection and avoidance of air traffic. Building upon collision avoidance systems development over the past decade for large UAS and manned aircraft, ACAS sXu provides such a capability, enabling autonomous and decentralized sUAS collision avoidance capability against manned aircraft, UAS, and other sUAS.

Detect and Avoid (DAA) systems are an essential technology to enable the integration of Unmanned Aircraft Systems (UAS) into the National airspace system (NAS). In the U.S., a DAA system's role is to fulfill the “see and avoid” requirement found in 14 CFR §91.113 (b), and the intent of U.S. standards is to enable UAS operations in all classes of airspace. Since the “Minimum Operational Performance Standards (MOPS) for Detect and Avoid Systems” (RTCA DO-365) were intended to enable operations in the U.S., however, the assumed operational environment may not apply to certain European nations, and the requirements therein must be assessed for applicability for any operational environment outside of the U.S. Additionally, EUROCAE WG-105's “Minimum Aviation System Performance Standards (MASPS) for Detect and Avoid [Traffic] (DAA[Traf])…” are not perfectly aligned with the U.S. MOPS; leaving gaps for future DAA manufacturers that wish to operate in both U.S. and European environments. For UAS to be integrated into both U.S. and European airspace, effort has been put towards aligning the standards of the respective bodies, and the applicability of an RTCA DO-365 compliant DAA system in European airspace has been assessed. Since the rules in each airspace class are standardized via ICAO “Annex 11: Air Traffic Services, Chapter 2, Section 2.6”, the classes of airspace are highly similar between all of the member states of ICAO, and the allocation of the classes is the primary difference between countries. The way this impacts operations and procedural mitigation is analyzed.

Using mobile devices for various flight planning tasks is becoming the norm among general aviation (GA) pilots. Mobile apps, such as ForeFlight and Garmin Pilot, provide services such as flight plan filing, electronic charts, and weather briefings, but time-consuming and error-prone voice-based methods must still be used to exchange clearance information with air traffic control (ATC). The ubiquity of mobile device and app use by pilots suggests that mobile applications could also be used to deliver Instrument Flight Rules (IFR) clearances and provide other traditionally voice-based services. This concept proposes that pilots will be able to exchange pre-departure and post-arrival information electronically at both towered and non-towered airports in the United States (U.S.), without speaking a single word. Similar to the Federal Aviation Administration (FAA) technology known as Data Communications (DataComm), used to supplement verbal communication with digitally delivered messages [1], mobile devices may provide an affordable option to GA pilots for pre-departure and post-arrival electronic information exchange. Both implementations provide a significant reduction in pilot and controller workload for the services they enable. Electronic information exchange may eliminate read-back errors and incorrect interpretations of spoken clearances for controllers across the country and the over 300,000 instrument-rated pilots in the U.S. (as of December 31, 2018) [2] to whom they deliver clearances. The concept consists of four parts: providing IFR clearance delivery, negotiating IFR release at non-towered airports, exchanging other pre-departure information, and cancelling IFR at non-towered airports. The MITRE Corporation (MITRE) created a research prototype, using flight plan information from the FAA's System Wide Information Management (SWIM) Flight Data Publication Service (SFDPS), to evaluate the concept's operational acceptability and investigate resulting benefits. It is expected that a fully electronic information exchange will significantly reduce pilot and controller workload. However, before the concept can be implemented as a capability available to users of the National Airspace System (NAS), some preliminary steps must be taken by the FAA, in conjunction with industry, to validate the concept to ensure success. In the summer of 2018, we partnered with ForeFlight, an electronic flight bag (EFB) technology provider, to design and conduct a 75-day field test of the mobile clearance delivery concept at Manassas Regional Airport (HEF). This study focused strictly on clearance delivery at a towered airport and was designed to be transparent to ATC. Participation in the test was available to pilots on a voluntary basis; pilots who opted into the study received an email from ForeFlight with expected clearance information. The goal was two-fold: 1) to show that it is possible for automation to generate accurate expected clearance information by comparing automatically generated clearances to the clearances provided verbally to pilots, and 2) to collect feedback from end users on the accuracy and acceptability of an electronic clearance delivery concept. Pilot subject matter experts listened to recordings for over 30 percent of the flights processed during the test period and compared the recorded clearances to the MITRE-generated data. The MITRE project team also reviewed subjective feedback from test participants. This field test successfully demonstrated that it is feasible to use SWIM data to formulate expected clearances and transmit that information to the pilot. Although we identified some mismatches between formulated clearance data and controller verbally issued clearances, analysis of the collected data allowed the team to determine the changes required to correct these mismatches and reliably send accurate clearance data to the pilot. Following the field test, we conducted table-top exercises to further explore the accuracy and effectiveness of delivering clearance information using a streamlined process that required minimal verbal exchange. We are also examining the end-to-end safety and cybersecurity implications of using mobile devices to exchange clearance information and will provide recommendations for developing a methodology to provide the assurance and security of data transmissions for mobile devices to enable these IFR services for GA pilots. Through these activities, we have identified challenges to making the mobile IFR services concept a reality. The most profound challenges are related to limitations within NAS systems, a mismatch between procedures and adaptation, and data elements that are unavailable to external systems. These technical challenges must be mitigated or overcome to enable these mobile IFR services.

Detect and Avoid (DAA) systems are integral to an Unmanned Aircraft System's (UAS) ability to Remain Well Clear (RWC) of other aircraft; they are an enabling technology for UAS to integrate into the National Airspace System (NAS). “Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) Systems” (RTCA DO-365 [1]) specifies requirements criteria for displaying conflict predictive information to the UAS Pilot in Command (PIC). General Atomics Aeronautical Systems, Inc. (GA-ASI), in conjunction with Information Systems Delft, developed an additional conflict predictive display mechanism called conflict probes. Current GA-ASI implementations of DAA have conflict probes displayed whenever a DAA heading band (a band generated on a traffic display to indicate hazardous headings) is displayed. Conflict probes predict the state of an intruder for a given heading, climb rate, and look ahead time. Effectively, rather than displaying the headings and altitudes/climb rates that will result in a DAA Loss of Well Clear (LoWC), they convey to the PIC the volume in space that correlates to a LoWc. This gives the PIC an area on their traffic display to avoid, rather than merely a heading. This allows the PIC more options to avoid a conflict, such as airspeed adjustment in certain geometries. These features may be helpful as a supplement to pre-existing guidance algorithms, or used as a DAA alerting and guidance algorithm on its own. The possibilities for implementation of conflict probes and the results on the effectiveness of DAA alerting and guidance are discussed. Anecdotal feedback from PICs utilizing this augmentation have preferred DAA heading bands with conflict probes. Subjective PIC data have corroborated that conflict probes are useful to the PIC in situations where Air Traffic Control (ATC) is not providing separation services, such as uncontrolled airspace. Further human factors studies are recommended.

Congestion is a problem at major airports in the world. Airports, especially high-traffic ones, tend to be the bottleneck in the air traffic control system. The problem that arises for the airspace planner is how to mitigate air congestion and its consequent delay, which causes increased cost for airlines and discomfort for passengers. Most congestion problems are fixed on the day of operations in a tactically manner using operational enhancements measures. Collaborative Trajectory Options Program (CTOP) aims to improve air traffic management (ATM) considering National Airspace System (NAS) users business goals, particularities faced by each flight and airspace restrictions, making this process more flexible and financially stable for those involved. In CTOP, airlines share their route preferences with the air control authority, combining delay and reroute. When CTOP is created, each airline might decide its strategy without knowledge of other airline’s flights. Current solutions for this problem are based on greedy methods and game theory. There is potential space to improve. This paper examines CTOP and identifies important strategic changes to ATM adopting this philosophy, particularly in Brazil.

In 2017 and 2018, under National Aeronautics and Space Administration (NASA) sponsorship, the New York Unmanned Aircraft Systems (UAS) Test Site and Northeast UAS Airspace Integration Research (NUAIR) Alliance conducted a year-long research project that culminated in a UAS technology flight demonstration. The research project included the creation of a concept of operations, and development and demonstration of UAS technologies. The concept of operations was focused on an unmanned aircraft transiting from cruise through Class E airspace into a high-density urban terminal environment. The terminal environment in which the test was conducted was Griffiss International Airport, under Syracuse Air Traffic Control (ATC) approach control and Griffiss control tower. Employing an Aurora Centaur optionally piloted aircraft (OPA), this project explored six scenarios aimed at advancing UAS integration into the National Airspace System (NAS) under both nominal and off-nominal conditions. Off-nominal conditions were defined to include complete loss of the communications link between the remote pilot’s control station on the ground and the aircraft. The off-nominal scenarios that were investigated included lost-link conditions with and without link recovery, an automated ATC initiated go-around, autonomous rerouting around a dynamic airspace obstruction (in this case simulated weather), and autonomous taxi operations to clear the runway.

The Federal Aviation Administration (FAA) publishes an annual Aerospace Forecast covering aviation activity in the National Airspace System (NAS), including en route Air Route Traffic Control Center (ARTCC) operational counts. The FAA uses the ARTCC forecast to inform staffing, budgetary, and other operational decisions for the facilities responsible for en route activity within the NAS.The ARTCC portion of the Aerospace Forecast is time-series based, utilizing historic counts of Instrument Flight Rules (IFR) aircraft handled by 21 ARTCCs and three Center Radar Approach Control Facilities (CERAPs). The existing forecasting methodology does not explicitly consider airline routes or passenger flows.The MITRE Corporation (MITRE), in collaboration with the FAA Office of Aviation Policy and Plans (APO), developed an ARTCC forecasting methodology using a quarterly origin-and-destination (O-D) passenger demand framework consistent with the Terminal Area Forecast (TAF). The TAF is the FAA’s official annual forecast of airport activity, which incorporates O-D passenger demand and other market data at airports with 100,000 or more annual commercial enplanements. The ARTCC forecast combines predicted O-D segment counts from TAF with historic flight path distributions derived from the FAA Traffic Flow Management System (TFMS).Existing forecasts do not explicitly account for variance in flight path distribution. This paper provides a description of the methodology for this O-D segment-based ARTCC forecast.

This paper describes an approach for estimating benefits of new air traffic control capabilities that are intended to reduce the amount of time required for air traffic controllers to complete certain tasks, thereby increasing transit capacity. Our approach is to examine how task time savings accumulate over time and lead to transit capacity increases that in turn mitigate congestion and delay. We define transit capacity as the maximum number of aircraft that a controller can safely and efficiently manage given the separation standards, airspace, and procedures within which controllers operate. Our approach combines several modeling, analysis, and statistical techniques to estimate benefits across the National Airspace System (NAS) enabled by new automation capabilities. The components of the approach include estimating task times and task frequencies, calculating workload, and transforming workload reductions to capacity gains that can be input into NAS-wide models.

In the present-day National Airspace System, the air traffic management system attempts to predict the trajectory for each flight based on the flight plan and scheduled or controlled departure time. However, gaps in trajectory data and models, coupled with tactical control actions that are not communicated to automation systems or other stakeholders, lead to trajectory predictions that are less accurate than they could be. This affects traffic flow management performance.Management by Trajectory (MBT) is a NASA concept for air traffic management in which every flight operates in accordance with a 4D trajectory that is negotiated between the airspace user and the FAA to account for the airspace user’s goals while complying with NAS constraints. The primary benefit of MBT is an improvement in system performance due to increased trajectory predictability and stability, which result from managing traffic in all four dimensions (2D route, vertical, and time), ensuring that changes to the flight’s trajectory are incorporated into the assigned trajectory, and utilizing improved time or arrival control standards. Importantly, the performance improvements support increasing efficiency without increasing collision risk.This paper provides an overview of MBT and describes fast-time simulation results evaluating the safety, performance, and efficiency effects of MBT.

For over 100 years manned aviation has been based on pilots seeing and avoiding other aircraft. During this time aviation has evolved to a point where there were 37.4 million commercial flights scheduled in 2014 [1]. The national airspace system (NAS) of each country is a complex system-of-systems involving air traffic control, a network of navigation and communication facilities, airports, controlled and uncontrolled airspace, and the associated rules and regulations for each part of this system. It is into this system that we are now introducing unmanned aircraft systems (UAS) for commercial and civilian applications.

As the technological and operational capabilities of unmanned aircraft systems (UAS) have grown, so too have international efforts to integrate UAS into civil airspace. However, one of the major concerns that must be addressed in realizing this integration is that of safety. For example, UAS lack an on-board pilot to comply with the legal requirement that pilots see and avoid other aircraft. This requirement has motivated the development of a detect and avoid (DAA) capability for UAS that provides situational awareness and maneuver guidance to UAS operators to aid them in avoiding and remaining well clear of other aircraft in the airspace. The NASA Langley Research Center Formal Methods group has played a fundamental role in the development of this capability. This article gives a selected survey of the formal methods work conducted in support of the development of a DAA concept for UAS. This work includes specification of low-level and high-level functional requirements, formal verification of algorithms, and rigorous validation of software implementations.

Among the premises of the Next Generation Air Transportation System initiative is the necessity for precision procedures in air traffic operations. Conformance with such procedures can help safety and the ability to accommodate higher traffic demand. The contribution of this paper is an algorithm for automating the process of speed control, aimed at separation assurance and conducted in current air traffic operations by human air traffic controllers. If a separation-compliant collective speed profile for the given set of aircraft exists, the algorithm computes this profile. If no such speed profile exists, the computer reports the nonexistence. Uncertainties such as weather are not considered, but the algorithm can be modified to include them. The algorithm proceeds in two stages. First, it finds an arrival sequence for the aircraft. The computational complexity of this stage is factorial in the number of aircraft but allows substantial parallelization. The second stage finds a collective speed profile fo...

Modernization of the National Air Traffic Control System is on the horizon and with it the likely introduction of autonomous air vehicles into the national airspace. With the increase in air traffic to an already congested management scheme, the implementation of a decentralized control protocol may be the answer to replace the current classical centralized management system. Equipping each aircraft with the information necessary to navigate safely through integrated airspace becomes an information-sharing problem: How much information about other aircraft is required for a pilot to fly the gamut of a heavily populated airspace safely? What paradigm shifts may be necessary for safe and efficient use of available airspace? This paper describes the development of a tool for testing alternative traffic management systems—centralized and decentralized—in the presence of uncertainty. Applying a computational fluid dynamics–inspired approach to the problem both creates a simulation tool to model the movement of traffic within the airspace and allows study of the effects of interactions between vehicles. With incorporation of a model based on smoothed particle hydrodynamics, discrete particle aircraft each carry a set of unique deterministic and stochastic properties. With this model, aircraft interaction can be studied for better understanding of how variations in the nondeterministic properties of the system affect its overall efficiency and safety. The tool is structured to be sufficiently flexible to allow incorporation of different governing equations (right-of-way rules) for aircraft traffic management.

The safety of air travel and air operations is critically linked to the availability of reliable aeronautical communications and navigation systems. This chapter describes the evolution of airport wireless communications systems from the legacy analog VHF AM system to the making of the AeroMACS concept. The background material that explains the reasons behind the emergence of this innovative aviation technology is described. The application of digital communications in civil aviation began with the introduction of Aircraft Communications Addressing and Reporting System (ACARS) technology in 1978. The National Airspace System (NAS) is a complex network of airports, airways, air traffic control facilities, and all the associated rules and regulation designed to supervise safety of flights for civil (commercial as well as private) and military aviation in the United States. The chapter briefly explores the dominant wireline signal exchange media on airport surface, that is, the fiber‐optic cable loop communications systems (CLCS).

Safety analysis of complex systems can be carried out using computational models of the subsystems and their interactions. A modeling framework for analyzing the safety of the national airspace operations is described. The framework enables investigators to collaboratively formulate models of the subsystems of the National Airspace System and insert off-nominal operating conditions to assess their impact on the overall system safety. The software is designed to allow popular computational frameworks such as Python and Java to interact with it in a seamless manner. Interfaces to one of the popular commercial computational package are also provided. The software incorporates detailed aspects of the national airspace system infrastructure such as airport taxi ways and runways, approach-departure procedures, jet routes, air traffic control Sector and Center boundaries. It also includes simplified human performance models of controllers, pilots and ground vehicle operators. Performance data for over 400 different aircraft types, derived from a well-known database have also been incorporated. Airspace flight operations data is obtained from the Federal Aviation Administration data feed, and the weather data is derived from the National Oceanic and Atmospheric Administration web site. Two example applications of the software are given.

This paper presents the first steps toward managing uncertainty and assessing risk within the national airspace system (NAS) by investigating the impact of uncertainty on “flight plan flexibility” (FPF) – a proposed quantitative measure of an aircraft’s ability to adapt its flight plan due to improbable events. First, an air traffic scenario derived from national flight plan data is simulated with an open source BlueSky air traffic control analysis centered on a busy airport. Next, state-space diagrams derived from the aircraft state parameters (i.e., speeds, altitudes, headings), spatial proximities, and surveillance signals are used to construct the FPF metric. Finally, a probabilistic analysis is used to propagate uncertainty in the aircraft positions through BlueSky to observe the resulting uncertainty in FPF through time. Future work will aggregate individual aircraft safety measures and additional metrics into a single system-wide indicator, transitioning from BlueSky to a gate-to-gate simulation for prognostics, and deriving probabilistic models of epistemic and aleatory sources of uncertainty in the NAS from available data.

The air traffic control (ATC) system is critical in maintaining the safety and integrity of the National Airspace System (NAS). This requires the information fusion from various sources. This paper introduces a hybrid network model called the Bayesian-Entropy Network (BEN) that can handle various types of information. The BEN method is a combination of the Bayesian method and the Maximum Entropy method. The Maximum Entropy method introduces constraints and is given as an exponential term added to the classical Bayes’ theorem. The exponential term can be used to encode extra information in the form of constraints. The extra information can come from human experience, historical data etc. These knowledges, once written in a mathematical format, can be incorporated into the classical Bayesian framework. The BEN method provides an alternative way to consider common data types (e.g., point observation) and uncommon data types (e.g., linguistic description for human factors) in the NAS. The reported work is demonstrated in two example problems. The first example involves an air traffic control network model and the BEN uses information from various sources to update for the risk event probability. The second example is related to the prediction of the cause of runway incursion. A network model studying different sources of error is used to make predictions of the cause of runway incursion. The training and validation data is extracted from existing accident report in the Aviation Safety Reporting System (ASRS) database. The results are compared with that of the traditional Bayesian method. It is found that the BEN can make use of the available information to modify the distribution function of the parameter of concern.

This paper presents a framework of unmanned aerial system (UAS) integration in the national airspace system (NAS). Its accurate definition of UAS is proposed first as well as how it is constituted. The whole scheme of UAS integrated in the NAS is described based on the formation of UAS. All the aircrafts flying in the sky are classified into two categories in the proposed framework. Two of the most important functions for each unmanned aerial vehicle (UAV) are introduced respectively, sense and avoid (SAA), and command, control and communication (C3) data link. Explicit structures and requirements are designed and illustrated. In our proposed method, ADS-B on satellite and on airships are presented for the first time by now. Based on the two ways UAS could operate all over the world, as well as accurate and safe flying. Data links between satellites exist in the whole system. Another any UAV at the terminal and en route could enjoy the air traffic services from air traffic controllers (ATC). And there are voice communications between ATC and UAV pilots in other places that called ground control station (GCS), which assures the information exchange in real time. According to the framework, UAS could be integrated in the NAS successfully, as well as assure all the operating aircrafts and anything on the ground safe.

This paper presents the methodology and results of a Human-In-The-Loop (HITL) simulation study conducted in the Airspace Operations Laboratory at NASA Ames Research Center. This study is a part of NASA's ongoing research into developing an Integrated Demand Management (IDM) concept, whose aim is to improve traffic flow management (TFM) by coordinating the FAA's strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The purpose of TFM is to regulate air traffic demand so that it is delivered efficiently through constrained airspace resources without exceeding their capacity limits. The IDM concept leverages a new TFMS capability called the Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for managing traffic tactically by generating precise arrival schedules. Unlike other TFM tools, CTOP gives flight operators the option of submitting a set of user-preferred alternative trajectories for each flight. CTOP can then use these trajectory option sets (or TOSs) to find user-preferred alternative routes to reduce demand on an overloaded resource. CTOP's effectiveness in redistributing demand is limited, however, by the availability of flights with alternative routes. The research presented in this paper focuses on evaluating the impact on TFM operations by varying the percentage of flights that submit a multiple-option TOS ('TOS participation levels'). Results show the impact on overall system performance and on the rerouted flights themselves. The simulation used a Newark (EWR) airport arrival scenario, with en route weather affecting traffic inbound from the west. Participants were asked to control each of the three arrival flows (north, west, and south) to meet their individual capacity constraints while simultaneously ensuring efficient utilization of the capacity at the destination airport. A large, permeable convective weather cell located southeast of Chicago severely reduced the capacity of the west flow. The study evaluated the impact of five different TOS participation levels on CTOP's ability to re-allocate traffic from the west and improve TFM performance in terms of delay assignment and traffic delivery rate to the airport. Overall, the results showed that increasing TOS submissions allowed the overall system delays to be reduced and fairly distributed among the three arrival flows, at the same time achieving the airport throughput rate. Moreover, it was found that aircraft who submitted a TOS saw a greater reduction in delay, even when they were assigned longer routes. This was particularly true when fewer aircraft submitted a TOS. The results confirm that the CTOP operations with higher TOS participation levels helped utilize the overall National Airspace System (NAS) resources as well as benefited the users who participated.

The integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS) poses a variety of technical challenges to UAS developers and aviation regulators. In response to growing demand for access to civil airspace in the United States, the Federal Aviation Administration (FAA) has produced a roadmap identifying key areas requiring further research and development. One such technical challenge is the development of a "detect and avoid" system (DAA) capable of providing a means of compliance with the "see and avoid" requirement in manned aviation. The purpose of the DAA system is to support the pilot, situated at a ground control station (GCS), in maintaining "DAA well clear" of nearby aircraft through the use of GCS displays and alerts. In addition to its primary function of aiding the pilot in maintaining DAA well clear, the DAA system must also safely interoperate with existing NAS systems and operations, such as the airspace management procedures of air traffic controllers (ATC) and Collision Avoidance (CA) systems currently in use by manned aircraft, namely the Traffic Alert and Collision Avoidance System (TCAS II). It is anticipated that many UAS architectures will integrate both a DAA system and a TCAS II. It is therefore necessary to explicitly study the integration of DAA and TCAS II alerting structures and maneuver guidance formats to ensure that pilots understand the appropriate type and urgency of their response to the various alerts. This paper presents a concept of interoperability for the two systems. The concept was developed with the goal of avoiding any negative impact on the performance level of TCAS II while retaining a DAA system that still effectively enables pilots to maintain DAA well clear. The interoperability concept described in the paper focuses primarily on facilitating the transition from a late-stage DAA encounter (where a loss of DAA well clear is imminent) to a TCAS II Corrective Resolution Advisory (RA), which requires pilot compliance within five seconds of its issuance. The interoperability concept was presented to 10 participants (6 active UAS pilots and 4 active commercial pilots) in a medium-fidelity, human-in-the-loop simulation designed to stress different aspects of the DAA and TCAS II systems. Pilots' ability to maintain separation, their rate of compliance and response times using the interoperability concept are reported. Results indicated that pilots exhibited comprehension of, and appropriate prioritization within, the DAA-TCAS II combined alert structure. Pilots demonstrated a high rate of compliance with TCAS II RAs and were also seen to respond to corrective RAs within the five second requirement established for manned aircraft. The DAA system presented under test was also shown to be effective in supporting pilots' ability to maintain DAA well clear in the overwhelming majority of cases in which pilots had sufficient time to respond.

To provide for the safe integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS), command and control (C2) links must be highly reliable. Hence, protected aviation spectrum is required to support such links for UAS that are integrated into controlled non-segregated airspace. For air-ground (i.e., non-satellite) links, protected aviation spectrum to support C2 links is available in the 960–1164 MHz (L) and 5030–5091 MHz (C) bands. The performance of any C2 system is critically dependent upon the characteristics of the air-ground (AG) channel. Therefore, as part of its UAS Integration in the NAS (UAS in the NAS) project, the U.S. National Aeronautics and Space Administration (NASA) performed a series of air-ground propagation flight tests to collect AG channel data for model development and analysis of potential C2 communications links capable of providing the required reliability. NASA's Glenn Research Center (GRC) conducted an extensive air-ground channel propagation measurement campaign (at altitude) for frequencies in the 960–977 MHz and 5030–5091 MHz ranges, for seven different terrain environments. The measurements were conducted in 2013, and produced the largest set of AG channel data ever gathered to date. This data was subsequently processed to develop models for the AG channel. The statistics collected enabled the derivation of channel model parameters for both narrowband and wideband channels. In order to make the propagation data widely available, the resulting narrowband statistics were processed and submitted to the International Telecommunications Union-Radiocommunication Sector (ITU-R) Study Group 3 Data Banks. Formats for data tables were developed, and tables of the aggregate narrowband propagation statistics for the seven ground site terrain environments were prepared, submitted to, and approved by, the ITU-R Study Group 3. This paper provides brief background on the measurement campaign, collection and processing of data, and development of the narrowband data tables. It further provides examples of the data and its use.