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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.

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.

Background: Considerable research is underway on how to integrate unmanned aircraft systems (UAS) safely and securely into the National Airspace System (NAS). While there is some ongoing research o...

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.

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 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.

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.

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...

Adverse weather impacts the safety and efficiency of aviation. Convective storms, turbulence, and icing are aviation weather hazards that can lead to unpleasant rides and, in the worst case scenari...

Weather creates numerous operational and safety hazards within the National Airspace System (NAS). In 2014, extreme weather events attributed 4.3% to the total number of delay minutes recorded by the Bureau of Transportation Statistics. When factoring weather’s impact on the NAS delays and aircraft arriving late delays, weather was responsible for 32.6% of the total number of delay minutes recorded. Hourly surface meteorological aviation routine weather reports (METARs) at major airports can be used to provide valuable insight into the likely causes of weather delays at individual airports. When combined with the Federal Aviation Administration’s (FAA’s) Operations Network (OPSNET) delay data, METARs can be used to identify the major causes of delays and to create delay climatologies for a specific airport. Also, patterns for delays and cancellations for the study period of 2003–15 can be identified for the individual airports included in this study. These patterns can be useful for operators and airport planners to optimize performance in the future.

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot-in-command of small unmanned aircraft systems should be able to maintain situational awareness ...

Natural hazards, such as hurricanes and winter storms, computer glitches and technical flaws, and man-made terror or cyber-physical attacks, can lead to localized perturbations of the U.S. national airspace system airport network (NASAN), which can in turn percolate across the interconnected system. Here we develop and demonstrate an approach to quantitatively characterize the robustness of NASAN, defined as loss of critical functions owing to perturbations, and a quantitative framework to select the most efficient and effective post-hazard recovery strategies. The system-level robustness and recovery strategies rely on network science methods and associated attributes. New insights include the central role of network attributes to robustness and optimal recovery sequences. Characterizations of robustness and fragility can inform what-if plans and proactive design, while recovery strategies developed in advance can support systematic, reliable, and timely bounce-back from hazard-related perturbations. The framework can serve as a baseline over which local information or cost optimization can be superposed.

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

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continually working to improve the safety of the operations. However, the current approach for identifying vulnerabilities in NAS operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to assess risk based on pre-defined safety vulnerabilities. Moreover, state-of-the-art machine learning models that are developed for event detection in aerospace data usually rely on supervised learning. However, in many real-world problems, such as flight safety, creating labels for the data requires specialized expertise that is time consuming and therefore largely impractical. To address this challenge, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised deep generative model for anomaly detection in high-dimensional time-series data. Validating on Yahoo&rsquo, s benchmark data as well as a case study of identifying anomalies in commercial flights&rsquo, take-offs, we show that CVAE outperforms both classic and deep learning-based approaches in precision and recall of detecting anomalies.

Urban Air Mobility (UAM) refers to a system of air passenger and small cargo transportation within an urban area. The UAM framework also includes other urban Unmanned Aerial Systems (UAS) services that will be supported by a mix of onboard, ground, piloted, and autonomous operations. Over the past few years UAM research has gained wide interest from companies and federal agencies as an on-demand innovative transportation option that can help reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. The concepts of UAM/UAS operation in the National Airspace System (NAS) remains an active area of research to ensure safe and efficient operations. With new developments in smart vehicle design and infrastructure for air traffic management, there is a need for methods to integrate and test various components of the UAM framework. In this work, we report on the development of a virtual reality (VR) testbed using the Cave Automatic Virtual Environment (CAVE) technology for human-automation teaming and airspace operation research of UAM. Using a four-wall projection system with motion capture, the CAVE provides an immersive virtual environment with real-time full body tracking capability. We created a virtual environment consisting of San Francisco city and a vertical take-off-and-landing passenger aircraft that can fly between a downtown location and the San Francisco International Airport. The aircraft can be operated autonomously or manually by a single pilot who maneuvers the aircraft using a flight control joystick. The interior of the aircraft includes a virtual cockpit display with vehicle heading, location, and speed information. The system can record simulation events and flight data for post-processing. The system parameters are customizable for different flight scenarios; hence, the CAVE VR testbed provides a flexible method for development and evaluation of UAM framework.

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain., Comment: 6 pages, 4 figures, 4 tables

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot in command of small unmanned aircraft systems should be able to maintain situational awareness ...

Queuing models provide an attractive and highly-efficient alternative to simulation for quantifying traffic flow efficiency. Stationary Markovian queuing models in which both inter-arrival times and service times are exponentially distributed have been studied by the National Airspace System (NAS). However, stationary queues cannot account for peaks and valleys in demand that are commonly observed in the NAS. Thus time-dependent Markovian queuing models, which aim to capture the variation in demand during a day, have been studied. Furthermore, statistical analysis of real traffic data reveals that inter-arrival times and service times do not follow exponential distributions. As a subclass of phase-type distributions, Coxian distributions with the advantage of closely approximating any distribution without violating the Markov property, have gained special importance on research in queuing systems. In this research, time-dependent Coxian queuing models Cm(t)(t)/Ck/s/s for modeling the en route phases of flight are developed as well, which are approximated by a piecewise constant Coxian inter-arrival time distribution and a time-invariant Coxian service time distribution. Both arrival rates and service rates are calibrated from data extracted from high-fidelity simulation runs driven by actual flying data. The number of aircraft in the system is regarded as a measure of the accuracy of queuing performance. Comparison results between time-dependent Markovian and Coxian queuing models are given in this paper. This study shows that time-dependent Markovian queues capture the variation in demand as well as Coxian queues, with the advantage of mathematical and computational tractability.

The UAS industry is experiencing a rapid expansion, doubling every year since 2013, as adopters of this disruptive technology find new ways to benefit from these aerial platforms (Spencer, 2018). Of particular interest is the use of small to medium sized UASs, with a cost to the user of less than $2,000 that are the catalyst for this growth. Despite this rapid growth, UAS operators claim an inability to use these systems to their full potential due to regulatory obstacles. A collection of industry experts were interviewed to discuss the UAS industry, and to explore these perceived obstacles that may be hindering an increased use of these systems across the United States. Represented in the body of 12 interviewees were UAS operators, academic experts, UAS reporters, and legal and regulatory professionals from across the country with on average at least five years of experience in UASs, and at least 10 years in aviation to provide a breadth of experience and array of viewpoints.

Flight on-time performance is one of the most important issues in the National Airspace System, a very complex and dynamic system. To avoid negative impacts to the aviation industry, the Federal Aviation Administration has set a long-term objective of understanding and mitigating flight delays. Building an effective and accurate prediction model of flight-delay incidents will help airport executives make the best decisions in delay scenarios. This article utilized two advanced prediction methods to predict the probability of a flight-delay incident—data mining using the decision tree and data mining using Bayesian inference. Prediction models were built using flight on-time performance data collected from different sources. The results indicated important airport-related factors and their effects on the flight on-time performance.

A unified collision avoidance control and coordination model for multiple unmanned aircraft systems (UAS) operating in close proximity is proposed. The collision avoidance behavior is based on the elastic collision of gas particles. A safety bubble around each agent is created by means of a sensor fusion architecture, enabling the Sense-and-Avoid (SAA) behavior of the collision avoidance control. The dimension of the safety bubble changes dynamically based on a quantified collision risk which is calculated based on the capabilities of the UAS and the number of neighboring agents. The weather conditions of the environment or airway, as well as the mitigation of third party casualties are also considered. The ultimate goal is to ensure the safety of operations performed by coordinated and uncoordinated UAS, as these systems become more popular in the National Airspace System (NAS).

This study is an aviation-based application of NOAA’s second-generation medium-range Global Ensemble Forecast System Reforecast (GEFS/R; i.e., hindcast or retrospective forecast) dataset. The study produced a downscaled probabilistic prediction of instrument flight conditions at major U.S. airports using an analog approach. This represents an initial step toward applications of reforecast data to probabilistic aviation decision support services. Results from this study show that even at the very coarse resolution of the GEFS/R dataset, the analog approach yielded skillful probabilistic forecasts of flight conditions (i.e., instrument flight rules vs visual flight rules) at most of the Federal Aviation Administration (FAA)’s Core 30 airports. This was particularly true over the central and eastern United States, including the important Golden Triangle, where aircraft flow affects traffic flow management across the entire national airspace system. Additionally, the results suggest that reforecast systems utilizing better horizontal and vertical resolution, in both the modeling system and the reforecast archive, would be very useful for aviation forecasting applications.

Applications for unmanned aircraft systems (UAS), or “drones,” are increasing rapidly. In order to provide safe and reliable links to integrate UAS into the National Airspace System (NAS), control and nonpayload communication (CNPC) system requirements are being specified. A comprehensive knowledge of the air-to-ground (AG) channels in the bands of interest (C-band and L-band) plays an essential role. The NASA Glenn Research Center has sponsored an AG channel measurement campaign for most of the typical ground site (GS) local environments, including over water [8] , hilly/mountainous [9] , suburban, and near-urban. As a continuation of our prior study, this paper addresses the suburban and near-urban scenarios. Our developed AG channel models include path loss, small-scale fading Ricean K factors, spatial and interfrequency correlations for multiple aircraft antennas, root-mean-square (RMS) delay spread, and wideband tapped delay line (TDL) models. The path loss is described by either log-distance or two-ray models, with small corrections for flight direction. The K factors were 12 (14) dB in L-band and 27.4 (28.5) dB in C-band in near-urban (suburban) environments. The interband signals were uncorrelated, but the intra-band signals were highly correlated, with the median correlation coefficient greater than 0.85. The C-band RMS delay spread was on average 10 to 60 ns, with maximum of approximately 4 μs. The TDL models are composed of the line-of-sight (LOS) component, a ground reflection, and up to seven intermittent multipath components (MPCs). Relative power, phase, occurrence probability, duration, and excess delays for these intermittent MPCs are quantified. An algorithm to simulate the AG channel impulse response (CIR) via the TDL models is provided.

Autonomous contingency management systems, such as a forced-landing system, which reacts appropriately to an engine failure, are important for the safe operation of unmanned aircraft systems. This ...

For unmanned aircraft systems to gain full access to the National Airspace System, they must have the capability to detect and avoid other aircraft. To safely avoid another aircraft, an unmanned ai...

The focus of this paper is to present a game theoretical modeling framework for the integration of unmanned aircraft systems into the National Airspace System. The problem of predicting the outcome of complex scenarios, where manned and unmanned air vehicles coexist, is the research problem of this work. The fundamental gap in the literature is that the models of interaction between manned and unmanned vehicles are insufficient: 1) They assume that pilot behavior is known a priori, and 2) They disregard pilot reaction and the decision-making process. The contribution of this paper is to propose a realistic modeling framework that will fill this gap. The foundations of the proposed method are formedbygame theory, which investigates strategic decision makingbetween intelligent agents; bounded rationality concept, which is based on the fact that humans cannot always make perfect decisions; and reinforcement learning, which is shown to be effective in human learning in psychology literature. An analysis of integration is conducted using an example scenario in the presence of manned aircraft and fully autonomous unmanned aircraft systems equipped with sense-and-avoid algorithms. © Copyright 2016 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

This is a technical note published in the Journal of Aerospace Information Systems [© by the American Institute of Aeronautics and Astronautics, Inc.].

High thin clouds are generated by airliners when hot exhaust gases and water vapor from the jet engines mix with cold, humid air to form high thin clouds. These anthropogenic (human made) condensation trails, or “contrails,” create a green-house effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. Although this effect is estimated to be

This paper extends on the initial findings of “APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE NATIONAL AIRSPACE SYSTEM - A PRIMER” (Stroup & Niewoehner: Herndon, VA; ICNS-2019), and looks at why the current technologies, enterprise architecture, and future program plans may not be enough to address persistent operational challenges. This paper further explores why emergent operational concepts, business models, and demand profiles may necessitate Artificial Intelligence (AI)-enhanced Communications, Navigation and Communications (CNS) infrastructure to disrupt current operational impediments. European airspace, as well as the NAS, has similar challenges. Key challenges explored in this study include: traffic flow management of diverse users; VAS Traffic Management - Air Traffic Management (UTM-ATM) airspace integration; equitable access to airspace; information exchange networks and airborne-ground interoperability of AI applications. Finally, we examine why trustworthiness and resiliency will be key mileposts on the regulatory pathway to AI certification.

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.

Technical requirements are currently under development for a detect and avoid system (DAA) that would support the operation of Unmanned Aircraft Systems (UAS) within the National Airspace System (NAS). Such a system would aid UAS operators in maintaining sufficient separation, or "well clear", from other aircraft in their vicinity. The first set of technical standards for a UAS DAA system (referred to as "Phase 1" requirements) was limited to UAS operations transiting through Class D, E, and G airspace to, or from, Class A (or special-use) airspace. Explicitly out of scope of the Phase 1 requirements was the operation of UAS within terminal airspace in the NAS. Fern, Rorie, Roberts and Monk (2018) explored the application of a Phase 1 DAA system within Class D terminal airspace in a human-in-the-loop simulation. The results of the study demonstrated a poor fit of the Phase 1 DAA well clear (DWC) definition to these terminal operations. The original DWC definition was designed for the en-route environment and could not account for the complex, tightly-choreographed nature of traffic typically found near an airport. The interaction of the en-route DWC definition and the terminal area traffic led to excessive DAA alerting against safely-separated traffic. The frequent alerting quickly degraded pilots' ability to discriminate between encounters where a maneuver was or was not necessary. This resulted in slower response times, higher rates of losses of DAA well clear, and more severe losses of DAA well clear than had been observed in previous DAA research (e.g., Rorie et al., 2017). The current paper reports on a follow-on study to Fern et al. (2018) that tested two DWC definitions designed to accommodate standard terminal area operating procedures, such as traffic in the downwind leg of a VFR traffic pattern while the UAS is on final. The two definitions utilized identical horizontal and vertical thresholds but differed in whether or not Tau (a function that takes into account the closure rate of two aircraft) was included as a part of the DWC hazard zone. The "Tau" DWC candidate definition nominally extended the DWC hazard zone by 15 seconds in the direction of the intruder aircraft, while the "No Tau" DWC candidate definition did not extend the DWC hazard zone. The results of this follow-on study indicated that both candidate definitions improved pilot and DAA system performance relative to Fern et al (2018). Pilot response times and rates of losses of DWC returned to levels comparable to earlier research in the en-route environment. Several important differences between the two DWC candidate definitions, however, are also discussed.

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.

A recent rash of near mid-air collisions coupled with the widespread proliferation of small unmanned aircraft systems (sUAS) raise concerns that integration is posing additional risk to the National Airspace System. In 2016, sUAS sighting reports by manned aircraft pilots averaged 147 per month. In the first three quarters of 2017, sUAS sightings jumped to 188 per month. The purpose of this study was to evaluate sUAS operator behavior to determine potential interference with aviation operations. While previous research has indeed yielded findings about operator behavior, such studies were generally based on data derived from Aviation Safety Reporting System filings or the UAS sighting report database maintained by the Federal Aviation Administration. In this study, the authors partnered with a UAS technology company to deploy an AeroScope, a passive radiofrequency detection device, to detect UAS flight activity in an urban area. While the device was limited to collecting flight information from only DJImanufactured platforms, it is estimated that the company holds a market share in excess of 70% providing a reasonable barometer for sUAS activity in the sample area. Over the 19-day sample period, the AeroScope device recorded 258 detections of 77 unique sUAS platforms. The authors assessed sUAS operator behavioral characteristics, including: UAS models, operating altitudes, preferred flying days and times, flight durations, and operating locations. The authors assessed 93 potential violations of 14 CFR 107 regulations, including controlled airspace breaches, exceeding maximum flight altitudes, and flight outside of daylight or civil twilight hours. The authors concluded that UAS activity in the sample area posed potential conflicts with a runway visual approach, created a collision hazard with three heliports, and heightened risk for visual flight rules operations underneath a controlled airspace shelf. The authors determined existing sUAS geofencing systems were ineffective at deterring sUAS activity unless they imposed flight restrictions in addition to hazard notification.

Minimum display requirements for Detect-and-Avoid (DAA) systems are being developed in order to support the expansion of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS). The present study examines UAS pilots’ subjective assessments of four DAA display configurations with varying forms of maneuver guidance. For each configuration, pilots rated the intuitiveness of the display and how well it supported their ability to perform the DAA task. Responses revealed a clear preference for the DAA displays that presented suggestive maneuver guidance in the form of “banding” compared to an Information Only display, which lacked any maneuver guidance. Implications on DAA display requirements, as well as the relation between the subjective evaluations and the objective performance data from previous studies are discussed.

Today’s National Airspace System (NAS) is facing a challenge of dealing with an increasingly larger number of flight operations. To address this, the Next Generation Air Transportation System (NextGen) is introduced by the Federal Aviation Administration (FAA) to improve the efficiency and safety of the NAS. Currently, the performance of the NAS is mainly measured using the delay-based metrics that cannot capture the positive aspects of the performance and level of utilization of the NAS. To address this issue, a diagnostic throughput factor analysis tool is proposed to analyze and quantify the factors that have greater responsibility for poor/better regions/times of performance of the en-route airspace using the concept of throughput. Through a function-level comparison with other applications such as ground transportation, manufacturing, and wireless communication, major factors affecting the NAS’s throughput performance are identified as Monitor Alert Parameter violation, aircraft conflict, metering, c...

As unmanned aerial systems (UASs) increasingly integrate into the US national airspace system, there is an increasing need to characterize how commercial and recreational UASs may encounter each other. To inform the development and evaluation of safety critical technologies, we demonstrate a methodology to analytically calculate all potential relative geometries between different UAS operations performing inspection missions. This method is based on a previously demonstrated technique that leverages open source geospatial information to generate representative unmanned aircraft trajectories. Using open source data and parallel processing techniques, we performed trillions of calculations to estimate the relative horizontal distance between geospatial points across sixteen locations., Comment: 15 pages, 7 figures, 4 tables, 19 references

Over the past decade Unmanned Aircraft Systems (UAS, aka “drones”) have become pervasive, touching virtually all aspects of our world. While UAS offer great opportunity to better our lives and strengthen economies, at the same time these can significantly disrupt manned flight operations and put our very lives in peril. Balancing the demanding and competing requirements of safely integrating UAS into the United States (US) National Airspace System (NAS) has been a top priority of the Federal Aviation Administration (FAA) for several years. This paper outlines efforts taken by the FAA and the National Aeronautics and Space Administration (NASA) to create the UAS Traffic Management (UTM) system as a means to address this capability gap. It highlights the perspectives and experiences gained by the University of Alaska Fairbanks (UAF) Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) as one of the FAA’s six UAS test sites participating in the NASA-led UTM program. The paper summarizes UAF’s participation in the UTM Technical Capability Level (TCL1-3) campaigns, including flight results, technical capabilities achieved, lessons learned, and continuing challenges regarding the implementation of UTM in the NAS. It also details future efforts needed to enable practical Beyond-Visual-Line-of-Sight (BVLOS) flights for UAS operations in rural Alaska.

The increasing number of small Unmanned Aircraft System (sUAS) encounters with manned aircraft or airports increases the risk of collision in the National Airspace System. The purpose of this research is to develop and test predictive models for sUAS violation incidents in NAS using machine learning. This research uses machine learning algorithms to predict the risk of sUAS violation incidents using the FAA's UAS sighting data with a sample size of 2088. Three sUAS violation types are identified: flying above 400 feet, flying with 5 miles from an airport, and flying in restricted airspace. Seven machine learning algorithms were used, including classification regression, decision tree, neural network, gradient boosting, random forest, Bayesian networks, and Memory-Based Reasoning. The results show that Gradient boosting produces the best predictive model. This model can predict the sUAS violation incidents with an accuracy of 95.7 percent. Location, distance to the airport, state, sUAs altitude, airport type, and aircraft type are the most influential predictors to the sUAS violation incidents.

International audience; This paper proposes an effective approach for modelling and assessing the risks associated with unmanned aerial vehicles (UAVs) integrated into national airspace system (NAS). Two critical hazards with UAV operations are considered and analyzed, which are ground impacts and midair collisions. Threats to fatalities that result from the two hazards are the focus in the proposed method. In order to realize ground impact assessment, a multifactor risk model is designed by calculating system reliability required to meet a target level of safety for different UAV categories. Both fixed-wing and rotary-wing UAVs are taken into account under a real scenario that is further partitioned into different zones to make the evaluation more precise. Official territory and population data of the operation scenario are incorporated, as well as UAV self-properties. Casualty area of impacting debris can be obtained as well as the probability of fatal injuries on the ground. Sheltering factors are not neglected and defined as four types based on the real scenario. When midair collision fatality risk is estimated, a model of aircraft collisions based on the density of civil flight in different regions over Chinese airspace is proposed. In the model, a relative collision area and flying speed between UAVs and manned aircraft are constructed to calculate expected frequency of fatalities for each province correspondingly. Truthful data with different numbers of UAVs is incorporated in the model with the expected number of fatalities after a collision is included. Experimental simulations are made to evaluate the ground impacts and midair collisions when UAVs operate in the NAS.

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.