Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Pritchett, Amy; Committee Co-Chair: Feron, Eric; Committee Member: Clarke, John-Paul; Committee Member: Tsiotras, Panagiotis; Committee Member: Yang, Bong-Jun

As airspace congestion becomes increasingly more common, one of the primary places airspace congestion is felt today, and will only continue to increase, is in areas where more than one major airport interact. We will call these groups of interdependent airports a metroplex; a term originally coined to describe large metropolitan areas where more than one city of equal (or near equal) size or importance. These metroplex areas are of particular importance in understanding future capacity demands because many of these areas are currently experiencing problems with meeting the current demand, and demand is only projected to increase as air travel becomes more popular. Many of these capacity issues have been identified in the FAA's Future Airport Capacity Task (FACT). From the second FACT report, it is stated that "the FACT 1 analysis revealed that many of our hub airports and their associated metropolitan areas could be expected to experience capacity constraints (i.e. unacceptable levels of delay) by 2013 and 2020, even if the planned improvements envisioned at that time were completed." This analysis shows that the current methods of expanding airports will not scale with the growing demand. To address this growing demand, a three part solution is proposed. The first step is to properly identify the metroplex areas to be evaluated. While the FACT reports serve to identify areas where capacity growth does not meet demand, these areas are not grouped into metroplexes. To do this grouping, an interaction metric was developed based on airport distance and traffic volume. This interaction metric serves as a proxy for how the existence of a second airport impacts the operation of the first. This pairwise metric was then computed for all commercial airports in the US and were grouped into metroplexes using a clustering algorithm. The second obstacle was to develop a tool to evaluate each metroplex as new algorithms were tested. A discrete event based simulation was developed to model each link in the airspace structure for each aircraft that enters the TRACON. This program tracks the delay each aircraft is required to accumulate in holding patterns or traffic trombones. A third and final method discussed here was an optimization program that can be used to schedule aircraft that are entering the TRACON to perform small modifications in their speed while en route to reduce the overall delay (both en route and in the TRACON). While formal optimization methods for scheduling aircraft arrivals have been presented before, the computational complexity has greatly prevented such algorithms from being used to schedule many aircraft in a dense schedule. This is because mixed integer programming (MIP) is a NP-hard problem. Practically, this means that the solution time can grow exponentially as the problem size (number of aircraft) increases. To address this issue, a Benders' decomposition scheme was introduced that allows solutions to be computed in near real-time on commodity hardware. These solutions can be evaluated and compared against the currently used TMA algorithm to show surprising gains in high density traffic.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Mavris, Dimitri N.; Committee Member: Baik, Hojong; Committee Member: DeLaurentis, Daniel; Committee Member: Lewe, Jung-Ho; Committee Member: Schrage, Daniel. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Air Traffic Management (ATM) aims at ensuring safe and efficient movement of aircraft in the airspace. The National Airspace System is currently undergoing a comprehensive overhaul known as NextGen. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision making is essential. These tools should aid in accommodating the air traffic throughput increase, while limiting controller workload and ensuring high safety levels. In the National Airspace System (NAS), the goal of en-route Traffic Flow Management (TFM) is to balance air traffic demand against available airspace capacity, in order to ensure a safe and expeditious flow of aircraft, both under nominal and perturbed conditions. The objective of this thesis is to develop a better understanding of how to analyze, model and simulate air traffic in a given airspace, under both nominal and degraded conditions. First, a new framework for en-route Traffic Flow Management and Airspace Health Monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale 3D flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation for optimizing en-route Air Traffic is proposed. It takes into account a controller taskload model based on flow geometry, in order to estimate airspace capacity. The simulations run demonstrate the importance of sector constraints and traffic demand patterns in estimating the throughput of an airspace. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid (VIL) are incorporated in the model, representing weather perturbations on the same data set used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. Simulations under perturbed conditions are then run according to different objectives. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model.

Perhaps no single industry has benefited more from advancements in computation, analytics, and optimization than the airline industry. Operations Research (OR) is now ubiquitous in the way airlines develop their schedules, price their itineraries, manage their fleet, route their aircraft, and schedule their crew. These problems, among others, are well-known to industry practitioners and academics alike and arise within the context of the planning environment which takes place well in advance of the date of departure. One salient feature of the planning environment is that decisions are made in a frictionless environment that do not consider perturbations to an existing schedule. Airline operations are rife with disruptions caused by factors such as convective weather, aircraft failure, air traffic control restrictions, network effects, among other irregularities. Substantially less work in the OR community has been examined within the context of the real-time operational environment. While problems in the planning and operational environments are similar from a mathematical perspective, the complexity of the operational environment is exacerbated by two factors. First, decisions need to be made in as close to real-time as possible. Unlike the planning phase, decision-makers do not have hours of time to return a decision. Secondly, there are a host of operational considerations in which complex rules mandated by regulatory agencies like the Federal Administration Association (FAA), airline requirements, or union rules. Such restrictions often make finding even a feasible set of re-scheduling decisions an arduous task, let alone the global optimum. The goals and objectives of this thesis are found in Chapter 1. Chapter 2 provides an overview airline operations and the current practices of disruption management employed at most airlines. Both the causes and the costs associated with irregular operations are surveyed. The role of airline Operations Control Center (OCC) is discussed in which serves as the real-time decision making environment that is important to understand for the body of this work. Chapter 3 introduces an optimization-based approach to solve the Airline Integrated Recovery (AIR) problem that simultaneously solves re-scheduling decisions for the operating schedule, aircraft routings, crew assignments, and passenger itineraries. The methodology is validated by using real-world industrial data from a U.S. hub-and-spoke regional carrier and we show how the incumbent approach can dominate the incumbent sequential approach in way that is amenable to the operational constraints imposed by a decision-making environment. Computational effort is central to the efficacy of any algorithm present in a real-time decision making environment such as an OCC. The latter two chapters illustrate various methods that are shown to expedite more traditional large-scale optimization methods that are applicable a wide family of optimization problems, including the AIR problem. Chapter 4 shows how delayed constraint generation and column generation may be used simultaneously through use of alternate polyhedra that verify whether or not a given cut that has been generated from a subset of variables remains globally valid. While Benders' decomposition is a well-known algorithm to solve problems exhibiting a block structure, one possible drawback is slow convergence. Expediting Benders' decomposition has been explored in the literature through model reformulation, improving bounds, and cut selection strategies, but little has been studied how to strengthen a standard cut. Chapter 5 examines four methods for the convergence may be accelerated through an affine transformation into the interior of the feasible set, generating a split cut induced by a standard Benders' inequality, sequential lifting, and superadditive lifting over a relaxation of a multi-row system. It is shown that the first two methods yield the most promising results within the context of an AIR model.

Air traffic demand is growing. New methods of airspace design are required that can enable new designs, do not depend on current operations, and can also support quantifiable performance goals. The main goal of this thesis is to develop methods to model inherent safety and control cost so that these can be included as principal objectives of airspace design, in support of prior work which examines capacity. The first contribution of the thesis is to demonstrate two applications of airspace analysis and design: assessing the inherent safety and control cost of the airspace. Two results are shown, a model which estimates control cost depending on autonomy allocation and traffic volume, and the characterization of inherent safety conditions which prevent unsafe trajectories. The effects of autonomy ratio and traffic volume on control cost emerge from a Monte Carlo simulation of air traffic in an airspace sector. A maximum likelihood estimation identifies the Poisson process to be the best stochastic model for control cost. Recommendations are made to support control-cost-centered airspace design. A novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely-used Traffic Alert and Collision Avoidance System (TCAS) is used to construct unsafe trajectory clusters. Results show that the inherent safety of routes can be characterized, determined, and predicted by relatively simple convex polyhedra (albeit multi-dimensional and involving spatial and kinematic information). Results also provide direct trade-off relations between spatial and kinematic constraints on route geometries that preserve safety. Accounting for these clusters thus supports safety-centered airspace design. The second contribution of the thesis is a general methodology that generalizes unifying principles from these two demonstrations. The proposed methodology has three steps: aggregate data, synthesize lean model, and guide design. The use of lean models is a result of a natural flowdown from the airspace view to the requirements. The scope of the lean model is situated at a level of granularity that identifies the macroscopic effects of operational changes on the strategic level. The lean model technique maps low-level changes to high-level properties and provides predictive results. The use of lean models allows the mapping of design variables (route geometry, autonomy allocation) to design evaluation metrics (inherent safety, control cost).

This dissertation presents a research effort to evaluate wake vortex mitigation procedures and technologies in order to decrease aircraft separations, which could result in a runway capacity increase. Aircraft separation is a major obstacle to increasing the operational efficiency of the final approach segment and the runway. An aircraft in motion creates an invisible movement of air called wake turbulence, which has been shown to be dangerous to aircraft that encounter it. To avoid this danger, aircraft separations were developed in the 1970s, that allows time for wake to be dissipated and displaced from an aircraft's path. Though wake vortex separations have been revised, they remain overly conservative. This research identified 16 concepts and 3 sub-concepts for wake mitigation from the literature. The dissertation describes each concept along with its associated benefits and drawbacks. All concepts are grouped, based on common dependencies required for implementation, into four categories: airport fleet dependent, parallel runway dependent, single runway dependent, and aircraft or environmental condition dependent. Dynamic wake vortex mitigation was the concept chosen for further development because of its potential to provide capacity benefit in the near term and because it is initiated by air traffic control, not the pilot. Dynamic wake vortex mitigation discretizes current wake vortex aircraft groups by analyzing characteristics for each individual pair of leader and follower aircraft as well as the environment where the aircraft travel. This results in reduced aircraft separations from current static separation standards. Monte Carlo simulations that calculate the dynamic wake vortex separation required for a follower aircraft were performed by using the National Aeronautics and Space Administration (NASA) Aircraft Vortex Spacing System (AVOSS) Prediction Algorithm (APA) model, a semi-empirical wake vortex behavior model that predicts wake vortex decay as a function of atmospheric turbulence and stratification. Maximum circulation capacities were calculated based on the Federal Aviation Administration's (FAA) proposed wake recategorization phase II (RECAT II) 123 x 123 matrix of wake vortex separations. This research identified environmental turbulence and aircraft weight as the parameters with the greatest influence on wake vortex circulation strength. Wind has the greatest influence on wake vortex lateral behavior, and aircraft mass, environmental turbulence, and wind have the greatest influence on wake vortex vertical position. The research simulated RECAT II and RECAT III dynamic wake separations for Chicago O'Hare International (ORD), Denver International Airport (DEN) and LaGuardia Airport (LGA). The simulation accounted for real-world conditions of aircraft operations during arrival and departure: static and dynamic wake vortex separations, aircraft fleet mix, runway occupancy times, aircraft approach speeds, aircraft wake vortex circulation capacity, environmental conditions, and operational error buffers. Airport data considered for this analysis were based on Airport Surface Detection Equipment Model X (ASDE-X) data records at ORD during a 10-month period in the year 2016, a 3-month period at DEN, and a 4-month period at LGA. Results indicate that further reducing wake vortex separation distances from the FAA's proposed RECAT II static matrix, of 2 nm and less, shifts the operational bottleneck from the final approach segment to the runway. Consequently, given current values of aircraft runway occupancy time under some conditions, the airport runway becomes the limiting factor for inter-arrival separations. One of the major constraints of dynamic wake vortex separation at airports is its dependence on real-time or near-real-time data collection and broadcasting technologies. These technologies would need to measure and report temperature, environmental turbulence, wind speed, air humidity, air density, and aircraft weight, altitude, and speed. / PHD

As air traffic congestion grows, air traffic flow management (ATFM) is becoming a great concern. ATFM deals with air traffic and the efficient utilization of the airport and airspace. Air traffic efficiency is heavily influenced by unanticipated factors, or uncertainties, which can come from several sources such as mechanical breakdown; however, weather is the main unavoidable cause of uncertainty. Because weather is unpredictable, it poses a critical challenge for ATFM in current airport and airspace operations. Convective weather results in congestion at airports as well as in airspace sectors. During times of congestion, the decision as how and when to send aircraft toward an airspace sector in the presence of weather is difficult. To approach this problem, we first propose a two-stage stochastic integer program by emphasizing a given single sector. By considering ground delay, cancellation, and cruise speed for each flight on the ground in the first stage, as well as air holding and diversion recourse actions for each flight in the air in the second stage, our model determines how aircraft are sent toward a sector under the uncertainty of weather. However, due to the large number of weather scenarios, the model is intractable in practice. To overcome the intractability, we suggest a rolling horizon method to solve the problem to near optimal. Lagrangian relaxation and subgradient method are used to justify the rolling horizon method. Since the rolling horizon method can be solved in real time, we can apply it to actual aircraft schedules to reduce the costs incurred on the ground as well as in airspace. We then extend our two-stage model to a multistage stochastic program, which increases the number of possible weather realizations and results a more efficient schedule in terms of costs. The rolling horizon method as well as Lagrangian relaxation and subgradient method are applied to this multistage model. An overall comparison among the previously described methodologies are presented.

Increased efficiency at airports is necessary to reduce delays and fuel consumption. Many of the busiest airports in the nation have at least one pair of closely spaced parallel runways (CSPRs), defined by a separation of less than 2500 ft, with one runway dedicated to arrivals and the other to departures. CSPRs experience a large decrease in capacity under instrument conditions because they can no longer operate independently. In order to mitigate this decrease in capacity and to increase efficiency, proposed herein is a departure regulator for runways so configured, along with a plan of study to investigate the effects of this regulator. The proposed departure regulator makes use of data from precision tracking systems such as ADS-B to issue automated or semi-automated departure clearances. Assuming sequential departure separations are sufficient for clearance, the regulator will automatically issue, or advise the controller to issue, the departure clearance as soon as the arrival on the adjacent runway has descended below its decision height. By issuing the departure clearance earlier, the departure regulator reduces the gap between a pair of arrivals that is required to clear a departure. By decreasing the gap, the regulator increases the number of opportunities where a departure clearance can be issued, given a particular arrival stream. A simulation models the effects of the regulator and quantifies the resulting increases in capacity. The simulation results indicate that all forms of the regulator would provide significant gains of between 14% and 23% in capacity over the current operating paradigm. The results also indicate that the capacity gains are greatest at high arrival rates. Therefore, implementation of the departure regulator could significantly decrease the congestion at many major airports during inclement weather.

In ongoing efforts to balance air traffic demand and airspace capacity, airspace complexity stands as a fundamental research problem. Taking a more analytic approach, this thesis proposes that airspace complexity can be described in terms of how the airspace (together with the traffic inside it and the traffic control algorithm) responds to disturbances. The response of the airspace to a disturbance is captured by the degree of control activity required to accommodate such disturbance. Furthermore, since the response of the airspace depends on the disturbance, this thesis introduces a complexity map which shows how an airspace responses to a set of different disturbances. Among the many possible types of disturbances, this thesis considers an aircraft entering into the airspace, and the proposed method of describing airspace complexity is illustrated with examples. The time evolution of a complexity map is investigated using a statistical approach. In addition, the proposed method is illustrated in relation to current and future traffic flow management concepts. It is also shown that the proposed method can be applied to airspace design problems.