The FAA initiated the Mini Global (MG) project in 2013 as a collaborative effort between the FAA and the international aviation community. This effort will provide a global networked infrastructure environment to research and validate System Wide Information Management (SWIM) concepts using the global exchange models including Aeronautical Information Exchange Model (AIXM), Flight Information Exchange Model (FIXM), and Weather Information Exchange Model (WXXM). The MG Phase I demonstration in 2014 engaged Air Navigation Service Providers (ANSPs) in both the Atlantic and Pacific regions. Currently in Phase II, the project will integrate regionally diverse Enterprise Messaging Service (EMS) providers to become the Global EMS (GEMS) messaging exchange infrastructure. MG's Phase II objectives also include complex scenarios and the development of new applications and services to benefit global aviation. These applications will be evaluated through the GEMS infrastructure to demonstrate their usefulness for future consideration and potential commercialization. This paper describes the development, integration, and demonstration of a Collaborative Trajectory Options Program (CTOP) application in the GEMS infrastructure.

Given its static and rigid structure, the current National Airspace System lacks the ability to cope efficiently with the increasingly severe demand capacity imbalances expected to develop over the coming years. To better accommodate the flexibility desired for future flight operations and to alleviate the demand capacity imbalances, research initiatives have been conducted under the dynamic airspace configuration concept. Although most past dynamic airspace configuration researchers have focused on en route airspace, this paper investigates terminal airspace operations. A dynamic sectorization algorithm for terminal airspace is developed, which combines a k-means clustering-based vertical sectorization algorithm, an integer-programming-based horizontal sectorization algorithm, and an a-shapes-based airspace sectorization algorithm. This dynamic sectorization algorithm is validated with real traffic data from several major international airports in the United States and simulated traffic data with projected future air routes and traffic patterns. Performance evaluation demonstrates that the algorithm can improve the efficiency of terminal airspace utilization and reduce traffic complexity.

As demand for air travel increases at a staggering rate, the National Airspace System (NAS) is becoming unable to effectively handle the increased stress on the system. To solve these inefficiencies, the Next Generation Air Transportation System (NextGen) has been proposed to improve the adaptability of the airspace. Because of the importance of the terminal airspace to the overall efficiency of the system, dynamically adapting the terminal airspace is crucial for adapting to future traffic patterns. An algorithm has been developed to use air routes and traffic data to dynamically generate sectors for various configurations and airports. This paper will evaluate the sectors created by our sector design algorithm for terminal dynamic airspace configuration (TDAC) using multiple methods. The first method will compare the performance of sectors generated with real traffic data and that of the current sectors. Using metrics inspired by dynamic density components, the traffic complexity can be estimated from current and algorithm generated sectors to quantify the algorithm’s success. The sector design algorithm will then be applied to future, simulated scenarios to determine if it will generate consistent results between real and simulated data. Finally, the effect of dynamic airspace configurations will be evaluated by analyzing the benefit of dynamical resectorization for various time intervals.

Current static routes, procedures and airspace within terminal areas contribute significantly to lateral and vertical path inefficiencies. These inefficiencies are estimated to be responsible for more than 8% of the fuel burned by flights within the United States [1]. Flexible or dynamic airspace in terminal areas offers substantial potential for benefit within both near-term and longer-term concepts of operation. To further NASA’s goals and to determine how much of dynamic airspace’s potential would likely be realized in various feasible implementations, the authors have developed Terminal Flow, a simulation-based environment for the study of dynamic airspace concepts. This paper describes the dynamic airspace concepts studied, their plausible implementation in the terminal area, Terminal Flow, our methodology, and the benefit estimates we obtained.

The future air travel demand will keep increasing at a steady rate, and the future flights and trajectory-based operations will become more flexible. Due to lack of flexibility, the current National Airspace System (NAS) does not have the ability to efficiently cope with the increasing air travel demand or to implement flexible use of future airspace. A lot of work has been done in the framework of Dynamic Airspace Configuration (DAC), aiming at dynamically configuring airspace to better accommodate the fluctuating air traffic demand and changing traffic pattern, but most past research has been focused on en route airspace. In this paper, a sector design algorithm is proposed for DAC in the terminal airspace, which combines a constrainted k-means clustering algorithm, integer programming techniques, and an alpha shapes based sectorization algorithm to improve the efficiency of airspace utilization and reduce the traffic complexity. Historical traffic data from major U.S. airports such as Hartsfield-Jackson Atlanta International Airport (ATL) and Dallas/Fort Worth International Airport (DFW) and simulated traffic data with future route designs and traffic patterns are used to validate the proposed algorithm and evaluate its performance.

Surface Collaborative Decision-Making (SCDM) is a process for data exchange to improve the efficient movement of arrivals and departures on and near the airport surface. The Surface CDM Concept of Operations (the ConOps) describes a vision for data exchange as well as a process for metering the flow of departures entering the movement area in order to reduce the need for physical departure queues [1]. This departure metering capability is known as Departure Reservoir Management (DRM).Under the DRM concept, flight operators provide and maintain an updated Earliest Off-Block Time (EOBT) for each flight indicating when the operator expects the flight to be ready to push back from the gate. The DRM assigns each flight a Target Movement Area entry Time (TMAT) when departure metering is in effect. The DRM selects the timing of the TMAT's to maintain a queue at the end of each departure runway of the Target Queue Length (measured in aircraft) whenever there is sufficient demand. If the demand and capacity of each runway are as forecast, and taxi-out and related processes occur as predicted, a queue of the Target Queue Length (measured in aircraft) will be maintained. The DRM capability is expected to be implemented at several airports in 2015 as part of the Federal Aviation Administration's (FAA's) Next Generation Air Transportation System (NextGen).When the information provided to DRM contains inaccuracies, maintenance of the target queue length may be compromised. In response to updates in poor information, DRM may re-adjust TMAT's in an attempt to maintain the desired queue. Updates to TMAT's present challenges to the flight operators who attempt to orchestrate aircraft loading, gate operations, passenger communications, crew times, and a variety of other factors in order to hit their assigned TMAT's. A variety of controls is envisioned in the ConOps to allow the Departure Reservoir Coordinator (DRC) to encourage TMAT stability while maintaining the desired queue lengths.In this paper we discuss the Surface CDM Simulation, a simulation model of airport performance that can be achieved under DRM as described in the Surface CDM Concept of Operations, as well as results and insights gained from the model. We show that the concept can work very well when provided with accurate, timely information from operators, but that inaccurate information can lead to undesirable outcomes. We also find that the different TMAT stability controls provided in the ConOps are of varying effectiveness. We expect these results to be useful to future operators of Surface CDM DRM capabilities, to the designers of tools enabling Surface CDM, and to developers focused on future refinements of the Surface CDM Concept of Operations.

This chapter reviews DCA model structure which looks at decision as a choice of course of action among the competing alternative actions. Once decision setting is done, then decision making is started after that. It also looks at how the elements of this decision context interact and the decision is also initiated when we become aware of an unrespectable state of nature and the decision making is the target condition. The alternate state of nature or the conditions, the triggers and problem diagnostics and under here: we look at the first step in developing the decision context that is to understand as precisely as possible the problems which need to be solved and their underlying causes. The diagnostic strategies to identify the decision context, inductive reasoning is essential if the correct objectives, alternatives and performance metrics which are to be identified are discussed. And some of the strategies used are variations' empiricist, generalist etc. The objectives as an expression of a desired condition or state of nature at the other end of the decision context are previewed. It also gives the identification of decision and alternatives.

The chapter provides an introduction to the tools of achieving a decision situation into a logical framework. The tools for achieving this include influence diagrams which are graphical tools for mapping the interaction of the various elements of a decision setting. With this, decisions are represented with rectangles, ovals, or circles. These may be measurable derived values that serve as inputs to a decision. The influence diagrams provide a useful snapshot of a decision setting and assist in building the analytical framework which is the decision tree. It also lays out some guild lines in its construction like only one branch can be chosen; each chance node must have branches that correspond to exhaustive outcomes. The basic decision tree forms which include basic risky decision, double risk decision, and range of risk, imperfect information, and sequential decision are explained. The decision tree and the decision uncertainty tree grouped with influence diagrams, graphically model a decision setting.

This paper describes an estimator called the Information Model that computes three types of information: tracks containing state estimates for individual objects that have been observed; geographic distributions of undetected objects that have not yet been directly observed but whose presence is suspected; and the geographic distributions of total object counts, including both tracks and undetected objects. Collectively, these outputs support a variety of decision-making processes about finding objects, monitoring objects, doing things to objects, or simply avoiding objects. As its inputs the Information Model accepts measurements from a broad class of sensors whose observations can be packaged as contacts, for example, Search Radar, Electro-optical, and Synthetic Aperture Radar sensors. The Information Model can integrate observations with widely varying geographic coverage, probabilities of detection, false detection rates, and update frequencies. It can also use additional, independent expectations of geographic distributions of the objects present. This paper defines the estimator’s inputs and outputs, describes its computations, and presents results derived from simulations.

This paper describes, a novel optimization-based approach to benefits analysis that allows us to analyze new collaborative air traffic management tools. We apply this method to analysis of the Collaborative Arrival Planner (CAP), a concept under development by NASA as part of a suite of decision-support tools for improved air traffic management. Central to our benefits analysis is an integer program, the Arrival Sequencing Model (ASM), which minimizes cost through adjustments to the arrival sequence. We use the ASM to evaluate whether CAP-enabled airline control over and/or visibility into the airport arrival sequencing process could improve overall performance of the air transportation system.