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NASA's Unmanned Aircraft System (UAS) Traffic Management (UTM) project aims to enable the integration of new aviation paradigms such as Unmanned Aircraft Systems (UAS) while providing the necessary infrastructure for future concepts such as On-Demand Mobility (ODM) and Urban Air Mobility (UAM) operations in the National Airspace System (NAS). In order to do so, the UTM project has developed an architecture to allow communication among UAS operators, UAS Service Suppliers (USS), Air Navigation Service Providers (ANSP), and the public. As part of this framework, the Supplemental Data Service Providers (SDSP) are envisioned as model and/or data based services that disseminate essential or enhanced information to ensure safe operations within low-altitude airspace. These services include terrain and obstacle data, specialized weather data, surveillance, constraint information, risk monitoring, etc. This paper highlights the development efforts of a non-participant casualty risk assessment SDSP called Ground Risk Assessment Service Provider (GRASP) which assists operators with pre-flight planning. GRASP is based on the previously introduced UTM Risk Assessment Framework (URAF) and allows UAS operators to simulate and visualize potential non-participant casualty risks associated with their proposed flight. The risk assessment capability also allows operators to revise their flight plans if the casualty risks are determined to be above acceptable thresholds. GRASP is configured to account for future improvements including servicing airborne aircraft as part of NASA's System-Wide Safety (SWS) project.

Small Unmanned Aircraft Systems (sUAS) pose a significant and increasing threat of collision with manned aircraft as they are further integrated into the National Airspace System (NAS). Current federal regulations for sUAS lay out rules for safe operation, including measures to prevent collision with manned aircraft under limited concepts of operation. However, these regulations are effective only to the extent that they are followed. While some sUAS have regulation-based limitations coded in their software (e.g., an altitude ceiling) to prevent violations, many regulations rely on the sUAS's operator's knowledge and compliance. Relying on regulation compliance to ensure safety is insufficient; multiple instances have occurred where initial detection of a trespassing sUAS was from a pilot visually spotting the sUAS. Visual detection additionally cannot be relied on during low visibility and low light conditions when visual detection of a sUAS would be extremely difficult. Instead, a surveillance system may be needed to identify trespassing sUAS to maintain safe distances between manned aircraft and sUAS to prevent loss of life or damage to property. This paper examines manned aircraft radar tracks from 2015 to quantitively evaluate and categorize airspace usage characteristics with regards to potential surveillance solutions.

Current method used for integrating space launch vehicles into National Airspace System (NAS) and reducing aircraft risks includes closing large air space areas to any air vehicles, known as hazard areas. Airspace regulations cause aircraft to reroute increasing flight distance, delays and overall flight cost. Air space restriction area and time are based on profile risk factors of space vehicle launch. This paper describes a method to dynamically construct a risk level map of commercial space launch operations' impact on nearby aircraft. The hazard area is divided into multiple sections, each section is also dynamically evaluated for a risk level, which is a comprehensive index considering the uncertain debris trajectory model and launch failure probabilities. This method provides a framework for modeling an optimal aircraft routing plan within a required risk level.

NASA's Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgements about a potential threat of a loss of well clear with another aircraft. NASA's Phase 1 research supported the development of a Detect and Avoid (DAA) system that supports the ground-based pilot's ability to detect potential traffic conflicts and determine a resolution maneuver, but existing display/alerting requirements did not account for multiple UAS control (1:N). Demands for increased scalability of UAS in the NAS operations are expected to create a need for simultaneous control of UAs, and thus, a new DAA HMI design will likely be necessary. Previous research, however, has found performance degradations as the number of vehicles under operator control has increased. The purpose of the current human-in-the-loop (HITL) simulation was to examine the viability of 1:N operations with the Phase 1 DAA alerting and guidance. Sixteen UAS pilots flew three scenarios with varying number of UAs under their control (1:1, 1:3, 1:5). In addition to their supervisory and sensor mission responsibilities, pilots were to utilize the DAA system to remain DAA well clear (DWC) during scripted conflicts of mixed severity. Measured response times, separation performance, mission task data, and subjective feedback were collected to assess how the multi-UAS control configuration impacted pilots' ability to maintain DAA well clear and perform the mission tasks. Overall, the DAA system proved surprisingly adaptive to multi-UAS control for preventing losses of DAA well clear (LoDWC). The findings suggest that, while multi-UAS operators are able to maintain safe separation (DWC) from other traffic, their ability to efficiently perform missions drastically decreases with their number of controlled vehicles. Pilot feedback indicated that, for this context, the use of automation support tools for completing and managing mission tasks would be appropriate and desired, especially for ensuring efficient use of assets. Finally, human-machine interface (HMI) design considerations for multi-UAS operations are discussed.

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.

Numerous operational paradigms, technologies, and missions are emerging as newcomers to the National Airspace System (NAS) develop small Unmanned Aircraft Systems (sUAS), personal air vehicles and other Urban Air Mobility (UAM) concepts. As the list of applications expands, maintaining the safety of the current airspace system remains one of the core concerns preventing widespread commercial implementation of these concepts. Further, the risks associated with unmanned aircraft operations themselves have to be recognized and mitigated in a timely manner. Safety-critical risks include, but are not limited to, flight outside of approved airspace, unsafe proximity to people or property, critical system failures, loss-of control, and cyber-security related risks. Instead of reacting to accidents, a set of predictive and data-driven risk monitoring, assessment, and mitigation capabilities are envisioned to help capture and eliminate hazards as these systems become operational. NASA’s System-wide Safety project is performing R&D on such a safety assurance concept. As part of this concept, this paper describes an architecture that continuously monitors a diverse set of onboard and ground-based sources to estimate and predict non-participant casualty risk during flight. Timely identification of the changing nature of this risk can inform decision making processes to mitigate current and impending situations.

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.

To enable expanded and safer access for unmanned aerial vehicles in the National Airspace System, a reliable system to detect and track them needs to be established. This paper combines two radar systems into a single network to provide tracking of UAVs across a wide area. Each radar detects the UAV’s path and those detections are combined into tracks using a recursive random sample consensus algorithm. Outdoor flight experiments show the ability of the system to track a UAV across two different radar fields of view.

Unmanned Aerial Vehicles (UAVs) have great potential in civilian applications, such as package delivery, agriculture, and disaster management. The number of UAVs in these applications will continue to increase, and thus so does the importance of safely integrating them into the National Airspace System (NAS), with one key component being the management of UAV traffic. Agents may be given pre-planned paths from a mediator traffic management system, but a system must also be implemented to resolve conflicts arising from issues such as deviation from preplanned paths or uncooperative agents. Deviations can occur from any number of factors, and a proper traffic management framework must be capable of resolving these conflicts. Proposed here is a Detect-and-Avoid (DAA) system that will resolve conflicts between UAVs in the airspace in a distributed bidding based method. The results show this approach can be scaled to a system with a large number of UAVs, and will provide near-optimal solutions in a dynamic environment.

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.

This paper presents a novel study of deterministic error propagation in a stereo vision tracking system implemented by the Federal Aviation Administration (FAA) to produce accurate three-dimensional (3-D) flight tracks of aircraft during the visual approach segment of an instrument landing approach. The FAA is in the process of modernizing the National Airspace System (NAS) in the United States with the Next Generation Air Transport System (NextGen) project. This involves updating the means of navigation, detection of air traffic and airway assignment in all phases of flight. The Collision Risk Model (CRM) is the fundamental constituent that tracks Time and Space Position Information (TSPI) data to assess the probability of collision within a given airspace. The CRM is used to determine the separation standards of airborne aircrafts in order to minimize the risk probabilities. This paper presents the derivation of a risk model used by the FAA while it mathematically formulates and analyzes the gaussian error propagation of the underlying stereo vision tracking based on the placement coordinates of the optical sensors at airport terminals.

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.

High thin clouds are generated by aircraft when hot exhaust gases from the jet engines mix with cold, humid air. These anthropogenic (human made) clouds, known as condensation trails or "contrails," provide a net global warming effect. Although they block approximately 23% of the incoming shortwave radiation, they also reflect back to Earth approximately 33% of outgoing longwave radiation. Although the contrails generate only 2% of total anthropogenic radiative forcing they affect global warming immediately (unlike CO2 that affects global warning in 20-40 years). In this way contrails could be used to reduce global warming now to buy-time for CO2 mitigation initiatives to take effect. For this reason it is necessary to inventory contrails in the National Airspace System (NAS).This paper provides an inventory of contrails for the U.S. NAS for 2015. The analysis is based on publicly available weather data, flight surveillance track data, models of contrail formation and persistence, and models of net radiative forcing. During this period an average flight schedule of 24,095 flights in 365 historic weather days generated a daily average of 83.7K nautical miles of contrails. The contrails resulted in an estimated daily net radiative forcing of +7.08 mW/m2. The diurnal effect was minimal, but the seasonal effect was significant: 63% of the total Contrail Along Track Distance was generated from June to September. Furthermore, less than 25% of the flights generated contrails on a given day and the most of the contrails are generated in the south-eastern United States. The implications of these results for contrail mitigation are discussed.

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.

The ICAO Air Traffic Management Requirements and Performance Panel (ATMRPP) developed the Manual on Flight and Flow – Information for a Collaborative Environment (FF-ICE) and released it as ICAO Document 9965. Subsequently the ATMRPP developed the Provisions, Implementation Guidance, and amendments to the aforementioned documents for the first step of FF-ICE called FF-ICE/R1 (Planning). The ATMRPP is currently investigating the next steps in achieving full FF-ICE and has determined the subsequent step to be FF-ICE/R2 (Execution). During the Execution phase of a flight, trajectory negotiations between the eAU and the eASP can either require only automation or require the involvement of a controller. The FF-ICE/R2 concept applies only to strategic operations, where tactical operations are out of scope. In order to help scope and define the FF-ICE/R2 concept and to prepare the National Airspace System (NAS) and the global aviation community for adoption of FF-ICE/R2 operations, the Federal Aviation Administration (FAA) has initiated the FF-ICE/X project. This project explores FF-ICE/R2, including the point of delineation between strategic and tactical operations, and the point of transition from FF-ICE/R1 to FF-ICE/R2. This paper details the technical components of the upcoming FF-ICE/X demonstration within a single eASP, while the complementary paper, Operational Evaluation of FF-ICE/R2, explores the operational aspects of that same demonstration. Both papers recognize that the FF-ICE/R2 concept is still evolving.The FF-ICE/X project is part of the FAA’s effort to examine and evaluate the potential impacts of future capabilities supporting FF-ICE on the FAA’s modernization effort. As such, the project assumes that the technical operating capabilities will be selected from the 2030 timeframe. This requires the technical team to analyze the scheduled future capabilities of the NAS based on the NextGen Segment Implementation Plan (NSIP) and assess different potential architectures and system enhancements for those capabilities within the NextGen Florida Testbed (FTB). To achieve this goal the technical team will identify what capabilities are directly relevant to FF-ICE/R2, what gaps exist at the FTB and what capabilities are feasible to implement in the lab environment. The technical effort will culminate in two demonstrations at the FTB and include mixed mode environments. Specifically, the first demonstration will be in the regional environment and include only one Enhanced ATM Service Provider (eASP). The second demonstration will be in the international environment and include multiple eASPs.The Execution phase includes the post-departure negotiation and ground trajectory synchronization. In the 2030 timeframe it is anticipated that pre-departure processes will primarily be automated and that the post-departure phase will still require human intervention. Although human intervention is expected while in post-departure, the Execution phase offers the eAU opportunities to employ their automation beyond the negotiation horizon. Because of this, two critical areas of technical investigation are the transition from Planning phase to Execution phase, and the point of delineation between FF-ICE/R2 and tactical operations. The technical team will explore message exchanges in the transition phase in an effort to determine the criteria for when a flight is expected to be in the Execution Phase. The team will also explore the post-departure negotiations with supporting TBO capabilities during the Execution Phase. For each message exchanged, the team will develop decision trees to track their unique flows. Exploring message exchanges will also help the team provide feedback to the FIXM Working Group for developing the data model and developing any new message types or elements in support of FF-ICE/R2.Finally, the technical team will consider the interface between the technical FF-ICE/R2 capabilities and the airspace users. It is understood that the majority of airspace users operate a network of complex and interdependent schedules with advertised timetables, crew schedules, maintenance schedules, ground handling, and more. It is of high value to the airspace user to have timely and accurate information that is standardized and globally available. The team will highlight the technical benefits that FF-ICE/R2 may provide to airspace users in making better and more informed decisions through a clearer vision of the operating environment and expanded time horizons.

For successful integration into the National Airspace System (NAS), Remotely Piloted Aircraft (RPA) need to have the capability to land automatically at a wide range of airports with varying infrastructure. Well-established systems such as the Instrument Landing System (ILS) are often not available at airports expected to be served by RPAs, and the Ground Based Augmentation System (GBAS) is currently only available at a limited number of public airports (two airports in the U.S. [1]). This paper examines the feasibility of an aircraft-based augmentation to Global Navigation Satellite Systems (GNSS) navigation that does not require ground-based equipment, for the purpose of enabling precision approach and landing for RPAs.

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.

The National Airspace System (NAS), as a portion of the US’ transportation system, has not yet begun to model or adopt integration of Artificial Intelligence (AI) technology. However, users of the NAS, i.e., Air transport operators, UAS operators, etc. are beginning to use this technology throughout their operations. At issue within the broader aviation marketplace, is the continued search for a solution set to the persistent daily delays and schedule perturbations that occur within the NAS. Despite billions invested through the NAS Modernization Program, the delays persist in the face of reduced demand for commercial routings. Every delay represents an economic loss to commercial transport operators, passengers, freighters, and any business depending on the transportation performance. Therefore, the FAA needs to begin to address from an advanced concepts perspective, what this wave of new technology will affect as it is brought to bear on various operations performance parameters, including safety, security, efficiency, and resiliency solution sets. This paper is the first in a series of papers we are developing to explore the application of AI in the National Airspace System (NAS). This first paper is meant to get everyone in the aviation community on the same page, a primer if you will, to start the technical discussions. This paper will define AI; the capabilities associated with AI; current use cases within the aviation ecosystem; and how to prepare for insertion of AI in the NAS. The next series of papers will look at NAS Operations Theory utilizing AI capabilities and eventually leading to a future intelligent NAS (iNAS) environment.

As projections indicate, the U.S. National Airspace System (NAS) vulnerability to lightning strikes is growing due to increased severity and frequency of convective storms. Lightning hazards pose risks to personnel safety and may cause power outages, and therefore they can impact reliability and performance of the airport’s facilities if they are not appropriately fortified. Although lightning-flash data with high temporal and spatial accuracy is available for the continental U.S., measuring airport facility lightning-induced outages remains challenging. Studies have shown that lightning-induced outages have been under-represented in the Federal Aviation Administration’s (FAA) official outage classification data [1], [2]. In our study we try to understand the relationship between lighting strikes and airport facility outages, and to determine the sensitivity thresholds of lightning hazards from a spatial and temporal perspective. We use data from the National Lightning Detection Network (NLDN) as well as the FAA facility outages. Although the Baltimore-Washington International (BWI) airport is used as a case study, our proposed method can easily be expanded to other airports in the NAS. By applying a spatial-temporal distribution analysis of lightning strikes and facility outages from 2009 to 2017, we propose a new classification of lightning-induced outages using machine learning (ML) algorithms. Our results show that approximately 20% of BWI outages have been misclassified, thus corroborating the tendency of underestimation of lighting impact to the NAS. Furthermore, we identify lightning hazard thresholds that help estimate the sensitivity of the airport’s facilities to lightning-strike peak intensity and multiplicity. The study findings can be used to assist facility managers in creating better mitigation strategies through adequate lightning protection, grounding, bonding, and shielding (LPGBS), while maintaining reliable and safe operations in the NAS.

With the integration of unmanned aircraft systems into the U.S. National Airspace System, low altitude regions are being stressed in historically new ways. The FAA must understand and quantify the risk of UAS collision with manned aircraft during desired low altitude unmanned operations in order to produce regulations and standards. A key component of these risk assessments are statistical models of aircraft flight. Previous risk assessments used models for manned aircraft based primarily on Mode C-based secondary surveillance radar observations. However, these models have some important limitations when used at low altitude. We demonstrate a methodology for developing statistical models of low altitude manned flight or applicable at low altitudes that leverages the OpenSky Network, a crowdsourced ADS-B receiver network that provides open access to the aircraft data, and the FAA aircraft registry, an open database of registered aircraft. Unlike Mode C surveillance, a key advantage to this method is the availability of necessary metadata to distinguish between different types of low altitude aircraft. For example, previous models did not discriminate a large commercial aircraft transiting to higher altitudes from low altitude or small general aviation aircraft cruising at low altitudes. We use an aircraft's unique Mode S address to correlate ADS-B reports with aircraft type information from the FAA registry. We filter surveillance data and statistically characterize the low altitude airspace based on aircraft type, performance, and location. Lastly, we leverage the characterization and aircraft tracks to develop a Dynamic Bayesian Network that models the behavior of low altitude manned aircraft, an extension of previous aircraft modeling approaches that have employed Bayesian networks. By sampling representative trajectories from the Bayesian network, we can model encounters between manned and unmanned aircraft at low altitudes to assess collision risk, a key supporting technology to support safe integration of unmanned aircraft.

To safely integrate the emerging Unmanned Aircraft Systems (UAS) technology into the national airspace system and a variety of application fields, the Federal Aviation Administration (FAA) is in charge of publishing rules to regulate and oversee UAS activities in the U.S. However, incompliance with rules undermines the effectiveness of such regulations. This study focuses on investigating the status of UAS regulatory compliance in the U.S. from the perspectives of UAS ownership registration and maximum flight altitude limitation, analyzing the socio-demographic factors of UAS users and their relationship with regulatory compliance. A U.S. national representative survey was performed for data collection. Descriptive statistics illustrate the overall status of UAS regulatory compliance and the demographic characteristics of the adult UAS users. Canonical correlation analysis was adopted to explore the pattern between variables of regulatory compliance and socio-demographic factors. Logistic regression was used to identify influencing socio-demographic factors for compliance behavior of UAS users on two specific rules. Results present 1) the demographic features of the adult UAS users in the U.S., 2) the overall status of UAS regulatory compliance in terms of ownership registration and maximum flight altitude limitation, and 3) the relationship between UAS regulatory compliance and influencing socio-demographic factors. Findings of this study provide aviation regulation authorities and UAS law enforcement important insights into UAS regulatory compliance to further develop more effective policies and strategies to safely and sustainably regulate UAS activities.

Because of recent technological advancements, a growing number of unmanned aircraft systems (UASs) are anticipated to occupy the U.S. National Airspace System (NAS) and operate side-by-side with human pilot controlled civil aircraft. UAS technology has transitioned to broader applications, including commercial, scientific, and expanded military use. There have been significant challenges concerning the safe and suitable integration of UASs with existing systems. The interaction between humans and increasingly automated systems is of concern to researchers. Additionally, the number of UAS sightings has increased significantly during the last few years. In this study, the research team compiled 7,400 reports of UAS sightings (2015–2018). The Latent Dirichlet Allocation (LDA) method was then applied to develop topics relevant to UAS sighting incidents. This study also developed an online interactive tool to show keywords associated with different topics. These interactive topic models can help policymakers establish new policies and regulations to address specific safety concerns.

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.

Unmanned Aerial Vehicles (UAVs) become promising in different civilian and military applications due to their lower cost and greater flexibility in comparison to manned aircraft. However, the growing diversity of flight makes UAVs vulnerable to mid-air collisions. To ensure the safety for manned aircraft, human pilots are responsible for detecting intruders in airspace and performing appropriate maneuvers to avoid collisions [1]. Unlike manned aircraft with human pilots involved, UAVs must be equipped with Sense and Avoid (SAA) systems to guarantee the flight safety [2]. Thus, SAA systems play an important role in merging UAVs into the National Airspace System. As illustrated in Figure 1, an SAA scheme is composed of four units, which are sensing, conflict detection, collision avoidance, and flight controller, respectively.

Today, unmanned aircraft system (UAS) operations are typically conducted in segregated airspace and in limited combat theaters. In order to integrate UAS into the National Airspace System (NAS), UAS must operate safely, efficiently, and be compatible with existing manned aircraft traffic so that the overall safety and efficiency of the airspace is not degraded [1]. There are many challenges that lie ahead to allow UAS unrestricted access to NAS, but perhaps the greatest technical challenge involves fulfilling the "See and Avoid" requirements (14CFR 91.111 and 91.113).

NASA has been developing and testing the Traffic Aware Strategic Aircrew Requests (TASAR) concept for aircraft operations featuring a NASA-developed cockpit automation tool, the Traffic Aware Planner (TAP), which computes traffic/hazard-compatible route changes to improve flight efficiency. The TAP technology is anticipated to save fuel and flight time and thereby provide immediate and pervasive benefits to the aircraft operator, as well as improving flight schedule compliance, passenger comfort, and pilot and controller work-load. Previous work has indicated the potential for significant benefits for TASAR-equipped aircraft, and a flight trial of the TAP software application in the National Airspace System has demonstrated its technical viability. As part of ongoing software research, development, and testing of the TAP software, we developed the capability to run the current build of the TAP software using the NASA Airspace and Traffic Operations Simulator (ATOS) with preloaded flight scenarios. Our demonstration is fully interactive and will allow participants to operate the TAP software on an iPad Air themselves, or observe the demonstrators operating it. The purpose of developing this capability is not only to assist with testing during software development, but also to acquaint researchers and potential stakeholders with the TAP human machine interface and the functionality and capabilities of the software itself.

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.

Recent advances in sensing and computing technology has made unmanned aerial vehicles/systems (UAV/UAS) low cost but still increasingly capable of executing complex missions in challenging environments. They have gained popularity in a vast range of civilian applications, including search and rescue, disaster relief, and filming. Recently, the Federal Aviation Administration (FAA) has issued the Notice of Proposed Rulemaking (NPRM) on UAS certifications [1], indicating that a large number of UAS will be present in the National Airspace System (NAS) in the near future. The NASA UTM project is an effort on enabling low-altitude UAS flights [2]. Big value envisaged by Amazon Prime Air [3] happens only when the drone is able to fly itself tens of miles from the distribution center to people’s homes autonomously. One prerequisite for such flights is collision avoidance. Our research aims at drones that travel in class G airspace. This article is an exploration of the drone collision avoidance problem in urban areas. The main contribution is a safety control framework that enables a UAS to perform collision avoidance with manned aircraft during autonomous navigation.

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...

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...

Recent advances in unmanned and autonomous vehicle technology are accelerating the push to integrate these vehicles into environments, such as the National Airspace System, the national highway system, and many manufacturing environments. These environments will require close collaboration between humans and vehicles, and their large scales mean that real-world field trials may be difficult to execute due to concerns of cost, availability, and technological maturity. This paper describes the use of an agent-based model to explore the system-level effects of unmanned vehicle implementation on the performance of these collaborative human–vehicle environments. In particular, this paper explores the introduction of three different unmanned vehicle control architectures into aircraft carrier flight deck operations. The different control architectures are tested under an example mission scenario using 22 aircraft. Results show that certain control architectures can improve the rate of launches, but these improvements are limited by the structure of flight deck operations and nature of the launch task (which is defined independently of vehicles). Until the launch task is improved, the effects of unmanned vehicle control architectures on flight deck performance will be limited.

Trajectory-based operations is a NextGen program concept that uses trajectory prediction, scheduling tools, and improved methods for trajectory management to improve the capacity and efficiency of the National Airspace System. Metering operations are one element of trajectory-based operations and involve the sequencing and scheduling of aircraft to flow-constrained points or meter points. Aircraft trajectories are managed to the schedule to ensure aircraft-to-aircraft spacing across the meter point. Automation tools are being developed to support air traffic controller tasks in managing aircraft trajectories to meet scheduled times of arrival at the meter points. Uncertainties in trajectory prediction, such as those related to wind-forecast and aircraft-modeling errors, pose a significant challenge in the design of automation for trajectory management. This paper proposes a framework for exploring and designing metering strategies in the presence of trajectory uncertainties, in which tradeoffs between the...

This paper describes new methods to reach safety targets for sense-and-avoid sensors for unmanned aircraft systems by evaluating integrity and continuity risks. These methods can be used to set sensor requirements. The closest-point-of-approach distance and time to closest point of approach, τ, are used to measure the hazards associated with the intruder aircraft. The contribution to knowledge in this paper is a new method that uses hazard state estimates and estimate error to establish 1) the integrity risk of the sense-and-avoid system not detecting imminent loss of self-separation, and 2) the probability of false alert, the flight-path continuity risk. A sensitivity analysis evaluates the impact on integrity and continuity of sensor noise, range, and sample interval. The methods described in this paper can be used to set potential sense-and-avoid sensor requirements for unmanned aircraft system integration into the National Airspace System.

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.

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.

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).

Understanding aviation transportation infrastructure system behavior and coupling with communication networks is essential for securing and restoring functionality against cyber-enabled threats. While significant progress has been made in the past decade on developing infrastructure resilience theories based on network structure and operations, translating and generalizing them to real-world practice has often been challenging due to imperfect data and inapplicability of modeling assumptions. These typically include: 1) stylized network structures without uncertainty, 2) node homogeneity, 3) static criticality measures, and 4) unrealistic cascade models originating from single points of failure. This paper presents the modeling perspectives and approaches that aim to address these theory-to-practice challenges using a well-grounded network-of-networks (NoN) construct. Real-world modeling challenges are identified and a network theory-guided conceptual NoN model is developed that may be operationalized with the U.S. national airspace system airport network and Federal Aviation Administration (FAA) communication network as an application domain.

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.

To address the Integrated Arrival, Departure, and Surface (IADS) challenge, NASA is developing and demonstrating trajectory-based departure automation under a collaborative effort with the FAA (Federal Aviation Administration) and industry known Airspace Technology Demonstration 2 (ATD-2). ATD-2 builds upon and integrates previous NASA research capabilities that include the Spot and Runway Departure Advisor (SARDA), the Precision Departure Release Capability (PDRC), and the Terminal Sequencing and Spacing (TSAS) capability. As trajectory-based departure scheduling and collaborative decision making tools are introduced in order to reduce delays and uncertainties in taxi and climb operations across the National Airspace System, users of the tools across a number of roles benefit from a real time system that enables common situational awareness. A real time dashboard was developed to inform and present users notifications and integrated information regarding airport surface operations. The dashboard is a supplement to capabilities and tools that incorporate arrival, departure, and surface air-traffic operations concepts in a NextGen environment. In addition to shared situational awareness, the dashboard offers the ability to compute real time metrics and analysis to inform users about capacity, predictability, and efficiency of the system as a whole. This paper describes the architecture of the real time dashboard as well as an initial proposed set of metrics. The potential impact of the real time dashboard is studied at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport (CLT). The architecture of implementing such a tool as well as potential uses are presented for operations at CLT. Metrics computed in real time illustrate the opportunity to provide common situational awareness and inform users of system delay, throughput, taxi time, and airport capacity. In addition, common awareness of delays and the impact of takeoff and departure restrictions stemming from traffic flow management initiatives are explored. The potential of the real time tool to inform users of the predictability and efficiency of using a trajectory-based departure scheduling system is also discussed.