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Aircraft loss of control (LOC) is a leading cause of fatal accidents across all transport airplane and operational classes. LOC can result from a wide spectrum of precursors (or hazards), often occurring in combination. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of conditions and uncertainties, including multiple hazards, and the validation process must provide a means of assessing system effectiveness and coverage of these hazards. This paper provides a detailed description of a methodology for analyzing LOC as a dynamics and control problem for the purpose of developing effective technology solutions. The paper includes a definition of LOC based on several recent publications, a detailed description of a refined LOC accident analysis process that is illustrated via selected example cases, and a description of planned follow-on activities for identifying future potential LOC risks and the development of LOC test scenarios. Some preliminary considerations for LOC of Unmanned Aircraft Systems (UAS) and for their safe integration into the National Airspace System (NAS) are also discussed.

This paper presents the concept of operations for interval management operations to be deployed in the US National Airspace System (NAS) by the Federal Aviation Administration (FAA) Interval Management Program. The arrivals and approach operations are explored in detail including the primary operation and variations. The use of interval management operations is described that begin in en route airspace and continue to a termination point inside the arrival terminal area in the highly automated terminal environment that includes other arrival management tools such as arrival metering, Ground-based Interval Management - Spacing (GIM-S), and Terminal Sequencing and Spacing (TSAS). The roles of Air Traffic and Pilots and the ground automation tools that are used by Air Traffic Controllers to enable the operations are explored.

Interval Management is a suite of ADS-B-enabled applications that allows the air traffic controller to instruct a flight crew to achieve and maintain a desired spacing relative to another aircraft. The flight crew, assisted by automation, manages the speed of their aircraft to deliver more precise inter-aircraft spacing than is otherwise possible, which increases traffic throughput at the same or higher levels of safety. Interval Management has evolved from a long history of research and is now seen as a core NextGen capability. With avionics standards recently published, completion of an Investment Analysis Readiness Decision by the FAA, and multiple flight tests planned, Interval Management will soon be part of everyday use in the National Airspace System. Second generation, Advanced Interval Management capabilities are being planned to provide a wider range of operations and improved performance and benefits. This paper briefly reviews the evolution of Interval Management and describes current development and deployment plans. It also reviews concepts under development as the next generation of applications.

In this paper, we research the feasibility of using high rate Automatic Dependent Surveillance – Broadcast (ADS-B) aircraft altitude and velocity information to detect the presence of mountain waves and Mountain Wave Turbulence (MWT) in the vicinity of steep terrain as well as atmospheric waves and turbulence from other sources that are of interest to aviation, for instance, Convective Induced Turbulence (CIT). The key element of ADS-B that enables the research is a 1 second update rate on ADS-B position reports, and aircraft position and altitude being reported based on Global Positioning System (GPS) accuracy. This frequency is much faster than today’s standard of reporting meteorological data via the Aircraft Meteorological Data Relay (AMDAR) or Meteorological Data Collection and Reporting System (MDCRS), and as we show, is fast enough to estimate the location of mountain wave events, MWT, and CIT. When combined with other weather state information gained by in situ sensors, satellite, and radar-based technology in the National Airspace System (NAS), a total situational awareness of mountain wave, MWT, and CIT information in the Continental United States (CONUS) can be achieved for supporting airline flight planning and Air Traffic Management (ATM) decision making.

Unmanned Aerial Vehicles (UAVs) are required to have Autonomous Collision Avoidance System (ACAS) to resolve conflicts, especially when flying in the National Airspace System (NAS). This paper focused on the non-cooperative concept for avoidance, where UAVs face rogue obstacle that does not cooperatively share their flight data, nor they follows the rule of the air. UAVswill rely on its on-board sensor for avoidance, in space and time range that is limited. The limitation make the entire process of sense, detect, and avoid (SDA) mostly not applicable, and sense and avoid (SA) manner is more preferable and safe. A method called SA-VO that extend the use of the known Velocity Obstacle (VO-) method is introduce to handle the problem. The method produce more efficient avoidance in the non-cooperative space than SA manner of avoidance, without fully run the SDA process. Probability of collision map based on ranges of obstacle range of positions and velocity is predefine to replace the detection part of SDA. The method is then tested using simulations of encounters between UAV and an obstacle. The simulations show that SA-VO can be a middle ground between the SDA and SA manner of avoidance.

Trajectory-Based Operations (TBO) is a NextGen concept, which involves increased use of trajectory prediction and scheduling tools and improved methods for trajectory management in order to improve the capacity and efficiency of the National Airspace System (NAS) and the flight efficiency and predictability of individual aircraft. Metering operations are one element of TBO and involve the sequencing and scheduling of aircraft to flow-constrained points, referred to as meter points. Aircraft trajectories are then managed to the schedule to ensure adequate 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 their scheduled times of arrival (STAs) 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 supporting automation for trajectory management. This paper proposes a framework for making trade-offs in metering automation control laws in order to manage aircraft trajectory uncertainty and balance the costs of intervention with the costs of meeting an STA with less than desired precision. This allows a metering operation to be formulated as an optimal control problem using the Bellman Equation and solved using Dynamic Programming. Numerical results are shown using uncertainties measured from operational data and representing actual trajectory-prediction errors. The optimal control approach is compared to alternate nonoptimal control laws to demonstrate the advantage of optimizing for the uncertainties. The results show that this optimal control framework is a viable approach for understanding and quantifying uncertainty trade-offs, enabling metering automation that supports more reliable and cost-effective operations.

compared to conventional scheduling problems. This paper presents a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA). It was first applied to a test problem introduced in existing literature. An experiment with a test problem showed that new methods can solve the 20 aircraft problem in fast time with a 65% or 440 second delay reduction using shared departure fixes. In order to test its application in a more realistic and complicated problem, the NSGA algorithm was applied to a problem in LAX terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial separation in current operations, which may introduce unnecessary delays. In this work, three types of separations ‐ spatial, temporal, and hybrid separations ‐ were formulated using the new algorithm. The hybrid separation combines both temporal and spatial separations. Results showed that although temporal separation achieved less delay than spatial separation with a small uncertainty buer, spatial separation outperformed temporal separation when the uncertainty buer was increased. Hybrid separation introduced much less delay than both spatial and temporal approaches. For a total of 15 interacting departures and arrivals, when compared to spatial separation, the delay reduction of hybrid separation varied between 11% or 3.1 minutes and 64% or 10.7 minutes corresponding to an uncertainty buer from 0 to 60 seconds. Furthermore, as a comparison with the NSGA algorithm, a First-Come-First-Serve based heuristic method was implemented for the hybrid separation. Experiments showed that the results from the NSGA algorithm have 9% to 42% less delay than the heuristic method with varied uncertainty buer sizes.

Inspired by the energy savings that migrating birds achieve when flying in formations, of interest is achieving a similar level of savings with aircraft flying in the National Airspace System (NAS). In particular, with typically more than 50,000 flights in the NAS on any given day, there is much clustering of aircraft so there is much potential for creating formations. Most previous research in this area has focused on analyzing aircraft in a single formation ‐ developing computational models, performing wind tunnel tests, or developing controllers for maintaining a formation ‐ but little research has been done on investigating a NAS-wide implementation of formation flight for commercial aircraft. This work leverages the corridor-in-the-sky concept from on-going aviation research to develop a model for simulating formation flight with these corridors. The results show that for the top ten corridors there are approximately 1,200 flights which can benefit from formation flight. Compared with actual flight plans the resulting annual savings is approximately $600 million and compared against flying optimal, great circle trajectories, the resulting annual savings is approximately $320 million. Finally, Monte Carlo simulation shows that these savings are robust to uncertainty and fluctuated flight schedules.

In this study, a Route-Based Turbulent-Weather Avoidance Model (TWAM-R) is presented. TWAM-R improves a previously reported trajectory-based model which was tactical in nature. TWAM-R considers more strategic turbulence-avoidance decisions that are based on turbulence forecasts and pilot reports (PIREPs). TWAM-R identifies areas of reduced capacity and areas of potential hazards due to congestion caused by air traffic avoiding clear-air turbulence. The algorithm is explained and illustrated using real-life turbulence and air traffic demand data. This study is a part of a larger effort aimed at the creation of weather impact translation models which specify the link between the weather hazard causes and their impact on the National Airspace System (NAS).

While each day in the National Airspace System is unique, even the most challenging traffic flow management situations are likely to have some historical precedent. Unfortunately, traffic managers today are mostly limited to their ownpast experiences, and cannot leverage the experiences of others. This paper describes a novel approach for leveraging such “lessons learned” by making past control actions in similar situations available to the decision maker, specifically in the context of potential ground delay programs. By reviewing the response in similar situations, the decision maker is better informed as to whether a ground delay program would be needed, and if so, what the duration, scope, and rates could be. In the work presented here, methods for searching a combined historical archive of weather, traffic and operational actions for comparable conditions are evaluated. Multiple Weather Impacted Traffic Indices, describing both observations and forecasts, are used to characterize the operational situation. The control action response is extracted from the National Traffic Management Log and posted advisories. We evaluate several schemes for finding relevant past situations by their ability to retrieve situations with the same control action as was ultimately chosen by the traffic managers. All but one scheme exceeded baseline performance in each retrieval category, with an average improvement of 10% on the range of the metrics we used, suggesting that an appropriate decision support tool could inform decisions on ground delay programs.

*† ‡ § This study analyzes the Air Traffic Management (ATM) impacts of surface/terminal weather constraints at the 35 busiest airports in the National Airspace System (NAS). The degradation in airport departure and arrival rate performance is investigated during time periods where ceiling, visibility, surface winds, freezing precipitation, and thunderstorms prevent clear weather throughput. Using cumulative distribution functions (CDFs), we compare airport performance during time periods when weather hazards are present with clear weather baseline performance. Comparing against the clear weather baseline as well as across weather hazard types allows us to rank the effects of various weather hazards. Weighting such results by the frequency of occurrence of the weather event allows us to identify the most costly weather hazards in the NAS for terminal area operations. Such information allows us to focus future ATM investments on those technologies that can best mitigate the most significant weather hazards affecting the NAS. All comparisons are performed with 2009 operational ATM data.

As new technologies are implemented to enhance the National Airspace System it is important to consider their impact on the environment and on the cost of operations. One of the major considerations for both is the amount of fuel consumed by aircraft. This paper describes a unique methodology that can be used to estimate the total fuel consumed by aircraft based on their 4-D trajectories and the weather through which they flew. This methodology is based on the European Organisation for the Safety of Air Navigation (EUROCONTROL) BADA (Base of Aircraft Data) fuel burn model. The paper then describes how the BADA fuel burn model and weather data provided by the National Weather Service were implemented in a prototype application and how this prototype was validated using actual data recorded during a flight for a specific aircraft. The paper concludes with a summary that includes suggestions for improvements that should be incorporated when implementing the production version of the application.

ight paths, the other including the entire National Airspace System. Numerical results of this study show that the Link Transmission Model achieves a same level of accuracy as Large-capacity Cell Transmission Model in air trac prediction , and the computational eciency is signicantly improved with an equally optimal solution in air trac optimization.

The effectiveness of future decision support tools for Traffic Flow Management in the National Airspace System will depend on two major factors: computational burden and collaboration. Previous research has focused separately on these two aspects without consideration of their interaction. In this paper, their explicit combination is examined. It is shown that when user preferences are incorporated with an optimal approach to scheduling, runtime is not adversely affected. A benefit-cost ratio is used to measure the influence of user preferences on an optimal solution. This metric shows user preferences can be accommodated without inordinately, negatively affecting the overall system delay. Specifically, incorporating user preferences will increase delays proportionally to increased user satisfaction.

The Dynamic Airspace Configuration (DAC) concept requires strategically organizing and efficiently allocating airspace. In the current National Airspace System (NAS), sector boundaries have been developed heuristically over decades in light of historical data and analysis. In our previous efforts, a graph model based on air route structure was developed to model the en-route airspace over the U.S., and was partitioned using a spectral clustering algorithm. This paper addresses how to generate sectors with desirable geometry using the partitioned graph as an input. The minimum distance constraints are considered in our twostep algorithm. Instead of converting these constraints into a mathematical programming problem as most previous research has done, we treat the constraints satisfaction as a geometric problem. In correspondence to vertices assignments, an algorithm is first proposed that aims to compute non-overlapping convex hulls, while satisfying the constraints. Then, we develop a novel method using the shortest path searching algorithm to create smooth sector boundaries outside the convex hulls, where the desirable boundary is mathematically defined and computed. Finally, the performance of the proposed sectorization algorithm is demonstrated using the Enhanced Traffic Management System (ETMS) air traffic data.

In the national airspace system, sectors get overloaded due to high traffic demand and inefficient airspace designs. Overloads can be eliminated in some cases by redesigning sector boundaries. This paper extends the Voronoi-based sector design method by automatically selecting the number of sectors, allowing three-dimensional partitions, and enforcing traffic pattern conformance. The method was used to design sectors at Fort-Worth and Indianapolis centers for current traffic scenarios. Results show that new designs can eliminate overloaded sectors, although not in all cases, reduce the number of necessary sectors, and conform to major traffic patterns. Overall, the new methodology produces enhanced and efficient sector designs.

The primary function of administering the United States’ National Airspace System (NAS) is the air traffic controller task of actively monitoring assigned aircraft and resolving the conflicts (i.e. losses of minimum separations between aircraft) anticipated some time in the future. The transformation of the current NAS is a planned evolution and will rely on communication digitally between air and ground and between aircraft with the use of enhanced trajectory-based operations and automation tools. To mitigate the safety risks of increased traffic growth and effectively designing automation to aid in the separation management task, research and development is required to improve the accuracy and usability of decision support tools. The paper presents the performance of an experimental strategic conflict probe model used in the development and validation of enhanced separation management concepts. Furthermore, this papers reports on a comprehensive design of experiment optimizing its performance and a comparison of its capability to an advanced operational system.

Most aircraft managed by air-traffic controllers in the National Airspace System are capable of flying parallel-offset routes. This paper presents the results of two related studies on the effects of increased use of offset routes as a conflict resolution maneuver. The first study analyzes offset routes in the context of all standard resolution types which air-traffic controllers currently use. This study shows that by utilizing parallel-offset route maneuvers, significant system-wide savings in delay due to conflict resolution of up to 30% are possible. It also shows that most offset resolutions replace horizontal-vectoring resolutions. The second study builds on the results of the first and directly compares offset resolutions and standard horizontal-vectoring maneuvers to determine that in-trail conflicts are often more efficiently resolved by offset maneuvers.

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

A linear time-varying aggregate traffic flow model can be used t o develop Traffic Flow Management strategies based on optimization algorithms. However, there are no methods available in the literature to translate these aggregate solutions into actions involving individual aircraft. This paper describes and implements a computationally efficient disaggregation algorithm, which converts an aggregate (flow-based) solution to a flight-specific control action. Numerical results generated by the optimization method and the disaggregation algorithm are presented and illustrated by applying them to generate TFM schedules for a typical day in the U.S. National Airspace System. The results show that the disaggregation algorithm generates control actions for individual flights while keeping the air traffic behavior very close to the optimal solution.

(Abstract) Federal Aviation Administration (FAA) air traffic flow management (TFM) decision-making is based primarily on a comparison of deterministic predictions of demand and capacity at National Airspace System (NAS) elements such as airports, fixes and en- route sectors. The current Traffic Flow Management System (TFMS) and its decision- support tools ignore the stochastic nature of the predictions. Taking into account uncertainty in predictions and moving from deterministic to probabilistic TFM is an important part of the NextGen program that will help TFM specialists make better and more realistic decisions. This paper uses current TFMS data to analyze how uncertainty in prediction of arrival times for individual flights translates into uncertainty in prediction of aggregate traffic demand counts at arrival airports. A methodology was developed for probabilistic prediction of aggregate 15-minute demand counts by using the probability distributions of arrival time predictions for individual flights. A key element of the methodology is that the aggregate demand counts are predicted from extended sets of flights with the estimated times of arrival (ETAs) in both the interval of interest and several adjacent intervals. Numerical examples are presented that illustrate the difference between deterministic and probabilistic traffic demand predictions.

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

*† This study analyzes aviation in-flight icing constraints and air traffic data in order to identify how those constraints translate into Air Traffic Management (ATM) impacts in the National Airspace System (NAS). A causality analysis aids in the creation of an in-flight icing impact translation model by specifying the link between the causes and the impacts that these constraints have on NAS resources. The causality analysis in this paper includes major safety and efficiency issues NAS users consider when making decisions due to in-flight icing. These impacts include cancellations, diversions, delays, holding, altitude selection, and decreased airport arrival and departure rates. The focus of this paper is to quantify the impacts that are identified in the causality analysis. Statistical methods are applied to check, when possible, that the causes and impacts have a statistically significant correlation.

*† An analysis of severe clear air turbulence-related weather constraints was performed to quantify the impact of these constraints on Air Traffic Management (ATM) in the National Airspace System (NAS). A number of specific severe turbulence situations in the NAS were investigated to validate and quantify the key relationships between causes and effects in a turbulence causality diagram. This study is a part of a larger effort aimed at the creation of weather impact translation models which specify the link between the causes of the weather constraints and their impact on the NAS, as well as expose major issues the NAS users face when making decisions in the presence of these constraints.

We examine the tradeo between fairness and delay in air trac o w management and planning, using an M/D/1 queue bank model. To motivate our o w-based approach to studying fairness, we rst give several illustrative examples that characterize fairness (and performance in terms of delay/cost) in the current United State National Airspace System. We then pursue analysis and design of fairness, using the queuebank model. We nd that optimal allocations of capacity resources (with respect to average-delay measures) are unfair: much larger delays are imposed on minor o ws as compared to major ones at the optimum. Also, the fairness of the optimal allocation is shown to exhibit a dependence, albeit a weak one, on the congestion level. We then explore how much additional delay cost is imposed if a certain fairness level is imposed, and nd that signican t improvement in fairness can sometimes be achieved with moderate increase in cost. Finally, we briey note that the fairness issue studied here is analogous with the fairness concerns that arise in bandwidth allocation problems for communication networks, and suggest that further examining these commonalities may be illuminating.

A generalized approach is proposed to support integrated traffic flow management decision making studies at both the U.S. national and regional levels. It can consider tradeoffs between alternative optimization and heuristic based models, strategic versus tactical flight controls, and system versus fleet preferences. Preliminary testing was accomplished by implementing thirteen unique traffic flow management models, which included all of the key components of the system and conducting 85, six-hour fast-time simulation experiments. These experiments considered variations in the strategic planning look-ahead times, the replanning intervals, and the types of traffic flow management control strategies. Initial testing indicates that longer strategic planning look-ahead times and re-planning intervals result in steadily decreasing levels of sector congestion for a fixed delay level. This applies when accurate estimates of the air traffic demand, airport capacities and airspace capacities are available. In general, the distribution of the delays amongst the users was found to be most equitable when scheduling flights using a heuristic scheduling algorithm, such as ration-by-distance. On the other hand, equity was the worst when using scheduling algorithms that took into account the number of seats aboard each flight. Though the scheduling algorithms were effective at alleviating sector congestion, the tactical rerouting algorithm was the primary control for avoiding en route weather hazards. Finally, the modeled levels of sector congestion, the number of weather incursions, and the total system delays, were found to be in fair agreement with the values that were operationally observed on both good and bad weather days.

This paper describes a class of weather-weighted periodic auto-regressive sector demand prediction models. The periodic auto-regressive model captures both the mid-term (thirty minutes to two hours) trend based on the historical data and the short-term (less than thirty minutes) transient response based on recent observations. For severe weather days, the model uses three-dimensional weather information, both storm locations and echo tops, to form a weather factor to adjust the predictions. Unlike traditional trajectory-based sector demand prediction methods, which predict the behavior of the National Airspace System adequately for short durations of up to twenty minutes and are vulnerable to the weather uncertainties, this class of models provides reliable short to mid-term sector demand predictions and account for the weather uncertainty.

The skies over the US are becoming more crowded every day. The current airspace system is under growing strain as the demand for air travel is steadily increasing, from over 740 million passengers flying in fiscal year 2006 to an estimated 1 billion passengers by 2015, according to Federal Aviation Administration (FAA) estimates. The current national airspace structure consisting of ARTCCs, sectors, etc has been defined several decades ago and barely delivers optimum results in the current-day heavy and concentrated traffic scenario. There may be many ways to optimize the current structure but there is always the need of finding a new structure which would serve not only the current air traffic requirements but also the expected 3 fold increase in air traffic as predicted by the FAA by 2025. A tube network has been studied in this paper as a possible way to re-define the air routes structure taking into consideration the current air traffic scenario and future capabilites of aircraft to maintain autonomous separation. An optimum number of tubes required have been derived using the point to point operations data for the top 500 airports in the National Airspace System (NAS).