Problem
Increasing efficiency at core airports is one of the primary goals of the Federal Aviation Administration (FAA). This research is targeted at improving efficiency through increased runway throughput. At airports where there is a dependency between arrival and departure operations, airport capacity can be lost due to the inefficient spacing of arrival aircraft. Departure capacity may be lost due to insufficient arrival spacing and arrival capacity may be lost because arrival aircraft are being spaced out when there are no aircraft waiting to depart. This applies primarily to same runway and crossing runway operations. With same runway operations the departing aircraft must be at least 6000 feet down the runway and airborne prior to the arrival crossing the landing threshold. For crossing runways the departure must have cleared the common intersection prior to the arrival crossing the landing threshold. Today at airports where this dependence exists approach controllers provide an agreed upon consistent interval between arrivals to ensure there is sufficient room to depart aircraft. The interval typically does not consider the inter-departure spacing requirements, the types of aircraft departing, or whether or not there are actually aircraft waiting to depart.

Objectives
This research is designed to create an automated method to analyze the surface departure queue and provide dynamic arrival aircraft spacing guidance to approach controllers to increase arrival and departure throughput for dependent operations. The automation will analyze the composition of the surface departure queue and determine the spacing between arrivals that best facilitates a more efficient departure schedule. The arrival spacing guidance will be presented to the arrival radar controller using the terminal automation system. The radar controller will receive visual guidance cues on their display that facilitate the most advantageous arrival spacing. It is envisioned that the concept will provide relevant information to the Local Controller in the tower and that the spacing guidance could be presented to flight crews in the cockpit using a display such as the Cockpit Display of Traffic Information (CDTI).

Activities
Initial efforts by MITRE have focused on exploring the potential benefits of the concept using the Total Airspace and Airport Modeler (TAAM). Our team is in the process of developing a concept prototype in the MITRE Corporation’s Center for Advanced Aviation System Development (MITRE/CAASD) Aviation Integration Demonstration and Experimentation for Aeronautics (IDEA) Laboratory. The prototype will allow us to further explore and develop the concept as well as conduct human in the loop (HITL) experiments using our Airport Traffic Control Tower and radar console simulation environments. We continue to explore and document the integration of our concept with other CAASD NextGen concept work, ongoing prototype development activities, and integration with existing FAA systems.

Impact
The application of this concept would provide an increase in total throughput at airports where the dependent conditions described above exist. MITRE’s concept development and prototype work is designed to provide direct input to the implementation of the capability in the FAA’s terminal automation platform.

Public Release No: 12-0843

Problem
The success of NextGen is dependent upon joint investments among multiple federal agencies, state and local-level governments, and private entities. While there is general agreement on the vision for the use of NextGen technologies to improve air transportation, the incentives for making investments are not always aligned with the perspectives of the individual stakeholders that must take part. This project is focused on understanding how policy decisions by the government can affect the way other stakeholders view the value of NextGen investments. We are looking specifically at National Airspace System (NAS) performance changes that could be affected by NextGen, and are developing a model for how airlines value such changes, in terms of the metrics that are relevant to this stakeholder group, and how forecast performance changes are likely to be translated to a monetized value.

Objectives
Our primary goals are to develop a better understanding of how airlines respond to forecast changes in the performance of the NAS, including understanding how they plan for and value prospective operational improvements. Key questions to be addressed include: How do airlines adjust scheduled block times in response to changes in actual block times? Given a projected change in actual block times, what schedule adjustments would we anticipate? How would such adjustments impact airline operational and financial performance? Our overall goal is to be able to evaluate whether a proposed NextGen improvement that requires joint investment is likely to be seen as favorable from multiple perspectives – the societal, long term view that reflects a government perspective and a short-term, business case view representing the likely range of responses from an investing airline’s perspective.

Activities
Our current work is expanding on our prior work on operational incentives and on understanding how flight operators value operational changes in the performance of the NAS. Our research will be building on analysis of past airline behavior, focusing on factors such as block time changes, scheduling practices, and revenue impacts using a wide range of available data. In addition, we plan to leverage collaborative relationships with a number of airlines. An airline response model should provide value to a number of work areas within MITRE, including work on evaluating NextGen portfolios, on NAS system performance modeling, and on broader modelig of the US.

Impact
The research done to date has already impacted the dialog within the aviation community, moving from discussion of operational incentives in a generic form to showing that there can be specific measurements of value. The desired outcome from this research is an improvement in FAA decision making by providing a better understanding of likely airline response to proposed NextGen technologies and the associated policies associated with those technologies. Improvements to the FAA's understanding of the likely response to policy decisions should improve the chances of success for programs requiring joint investment. We also believe that having an airline response model will have direct applicability to CAASD analysis efforts in modeling future system performance.

Public Release No: 12-0535

Problem
The Federal Aviation Administration (FAA) has indicated that surface surveillance can enhance controllers' situation awareness of the airport surface and improve the safety of operations on and around the runways. At 44 of our nation's large airports, the FAA is in the process of installing integrated surface surveillance capabilities (i.e., Airport Surface Detection Equipment - Model X; ASDE-X), which can provide the location of an aircraft on the airport surface to controllers in the tower. Because integrated surveillance systems use a combination of advanced capabilities (e.g., multilateration and surface movement radar), they are cost prohibitive for small airports, leaving over 450 towered airports in the United States without any such surveillance system. As a result, these small to medium airports are not able to take advantage of potential safety benefits.

Objectives
This research is designed to develop a block occupancy-based surface surveillance concept and prototype display using very inexpensive magnetic sensors, installed in existing taxiway and runway light enclosures, for surveillance input. Under this concept, runways and taxiways on the airport surface are divided into operationally-relevant blocks, and magnetic sensors are used at the block boundaries to monitor aircraft and ground vehicles entering and exiting these blocks. As a target moves on the taxiways and runways, the sensor detects such an entry into or an exit out of a block, the surveillance system identifies the occupancy status of each block, the status of the block is sent wirelessly to the tower, and a user interface presents this status information to the controllers.

Activities
Under this effort, MITRE is developing a block occupancy display prototype and a concept of operations for its use, integrating this prototype with an air traffic control tower simulation, and evaluating the prototype with experienced controllers. In the proposed study, researchers will examine the impact of visibility and traffic density to controller workload, situation awareness, task performance, and acceptance. The measures will be collected during routine operations and during off-nominal events (e.g., pilot deviation on the surface, foreign objects on the taxiway or runway). Empirical data will be used to refine the concept of operations and prototype system.

Impact
A successful development of an inexpensive surface surveillance system is envisioned to improve surface safety at small and medium-sized airports, where advanced surveillance capabilities will not be realized. The proposed surface surveillance system may also serve to support airport operations when visibility is low, to reduce the impact of field of view issues from the tower, or to improve situation awareness of the airport surface for non-towered airports.

Public Release No: 12-1086

Problem
As the FAA evaluates procedures and technologies for NextGen, an objective, real-time, and continuous measure of workload can be critical to assessing impact on pilot and controller performance. Any decrease in performance could result in reduced safety and efficiency of the NAS. Currently no such measure exists, but enhancements in electroencephalography (EEG) show promise. EEG measures brainwave frequencies across the cerebral cortex and patterns of frequencies provide a strong basis for metrics of cognitive workload, vigilance, and attention. Recent advances in EEG technology permit reliable measurements of brainwave frequencies with better data quality, system usability, and minimal intrusiveness.

Objectives
Our research is focused on developing EEG-based metrics that can be used during Human In the Loop Simulations (HITLs) to assess NextGen automation concepts in terms of operator workload, processing, and attention, and how these factors affect performance. The idea is to develop a set of objective, real time metrics based on EEG data to measure human workload and track changes in workload as tasks are performed. These metrics will provide a more direct way to assess the impact of ATC and flight deck technology on human cognition and its relation to performance.

Activities
In our first experiment, we evaluated relationships among a wide range of EEG metrics to performance of laboratory tasks requiring the same skills as ATC tasks. Subsequently, we applied those same EEG metrics to ATC performance by controllers in a medium fidelity ATC simulator. The ATC scenarios varied in air traffic complexity and traffic volume and participants were asked to provide subjective workload ratings. The EEG metrics showed linear increases with traffic volume and with subjective workload ratings but not with traffic complexity. These results suggest that a sensitive and robust, real time workload gauge (which we call “BrainGage”) could be applied to the task of air traffic control and other highly complex real-world tasks. Based on these results we are now building and validating an EEG-based workload gauge. This tool will enable us to see dynamic changes in workload as system variables change during post-simulation analysis or in real time as experiments are running. We will validate the BrainGage metrics in the context of an ATC HITL conducted in MITRE’s Aviation IDEA Laboratory.

Impact
This effort will help provide an objective metric to facilitate FAA decisions about candidate technologies that potentially impact human performance. As various NextGen research prototypes advance toward implementation, it will be very helpful to have objective evidence about their real impacts on human performance.

Public Release No: 12-1106

Problem
Trajectory Based Operations (TBO) is the NextGen concept of improving throughput, flight efficiency, flight times, and schedule predictability through better prediction and coordination of aircraft trajectories. Implementing TBO effectively requires understanding the interactions and trade-offs between proposed TBO capabilities, operations, and sources of uncertainty. For TBO, integrated benefits analysis has proven a serious challenge. System impacts and relationships between capabilities have proved difficult for analysts and decision-makers to visualize, and differences in intuition on trade-offs makes consensus building difficult. There has been no analytical framework for integrated understanding and measurement of TBO. This project is developing a mathematical framework for TBO in which benefits, constraints, and tradeoffs are described by the magnitude and timing of aircraft trajectory uncertainties, and by the impact on ATM performance metrics from uncertainties at different look-ahead times. We believe this framework can capture the most important performance features of TBO-related capabilities and their interactions.

Objectives
Our goal is to establish a means for representing all TBO-related capabilities and operations, across all phases of flight-planning and flight, within a common analytical framework. This framework must enable the benefits and constraints of TBO capabilities – individually and in combinations – to be predicted, measured, visualized, and more broadly understood. We have divided this goal into four technical objectives. The first is to identify the key performance features and interactions that must be represented in order to understand the behavior of multiple interacting TBO capabilities and operations. The second is to identify measures and analytic models capable of quantitatively describing these key performance features and interactions. The third is to demonstrate analysis of integrated networks of current and planned TBO capabilities and operations. Fourth is to demonstrate analysis of performance/benefit trade-offs for TBO capabilities within an integrated network. In addition, overall success of the effort will depend on transferring our concepts, methods, and tools into MITRE’s work for the FAA, and into the aviation research, analysis, and stakeholder communities at large.

Activities
In our earlier research we achieved our first and second technical objectives. The key TBO performance feature is longitudinal timing uncertainty as a function of look-ahead time, and the key interactions are changes and sensitivities to uncertainty. We quantify these with in terms of marginal uncertainty and variability discount functions, and we began developing analytical tools to measure and predict this uncertainty and its impacts. In our current research we have measured variability, component sources of variability, and uncertainty in historical aircraft operations and are observing differences between phases of flight and between different airspaces. Characterizing these as probability distributions allows us to analyze both frequent behaviors, and low-probability / high-impact “tail” behaviors. We have developed methods to integrate measured uncertainty distributions with analytic models of metering in order to predict delay distributions and costs, and we are integrating multiple stages of flight progress uncertainty and metering intervention to model end-to-end system behaviors.

Impact
Methods and tools to quantify performance/benefit trade-offs relative to uncertainty are applicable to many aspects of NextGen – including 4D trajectories and FMS standards, ground and airborne interval management, traffic flow management, and integration between these and other NAS and NextGen processes. We are analyzing test cases in each of these areas to demonstrate the analytical framework and to validate its value in MITRE’s work on NextGen.

Public Release No: 12-1120

Problem
There is a need for Unmanned Aircraft Systems (UAS) to routinely operate in non-segregated civil airspace without degrading safety and without acutely disrupting legacy airspace users. Given that there is no on-board pilot to perform “see and avoid” duties, a mechanism is needed to enable the unmanned aircraft to remain well-clear and avoid collisions. For times when the command and control (C2) link may not be available, the mechanism needs to be automatic (i.e., empowered to act without pilot involvement). While multiple sensing alternatives exist, a promising but under researched alternative is to leverage NextGen Automatic Dependent Surveillance – Broadcast (ADS-B) technology as a surveillance source for “sense and avoid”.

Objectives
The objective of this research is to explore the viability of cooperative automatic “sense and avoid” algorithms. Through a progressive series of experiments and flight demonstrations, we intend to explore the technical and operational issues associated with automatically ensuring a separation distance that both meets the "well clear" safety criteria and considers mission constraints and limitations.

Activities
With cooperative automatic “sense and avoid”, airborne equipment receives position information either directly from all nearby aircraft or via Traffic Information Service – Broadcast (TIS-B) messages. Onboard automation detects potential conflicts and/or collision hazards, determines the appropriate maneuver, executes the maneuver, and determines when to return to course. Since no direct pilot action is required to initiate the maneuver, the approach is not susceptible to vulnerabilities and latency in the C2 link. During this multiyear project, we intend to explore the feasibility of cooperative automatic "sense and avoid" using computer simulation and flight testing. With a MITRE developed fast-time computer simulation, algorithms are subjected to thousands of simulated flight encounters/conditions, and a fitness report records strengths, weaknesses, and overall performance. During flight-testing activities, algorithms are evaluated onboard both small (less than 55 pounds) UAS and a surrogate UAS—an aircraft that operates like a UAS but with a safety pilot onboard at all times. The surrogate UAS can be controlled remotely or via onboard automation. Flight evaluations will be used to both collect data and demonstrate to aviation stakeholders how readily available technology can be integrated to provide effective and affordable cooperative automatic “sense and avoid”. Furthermore, this research will produce operational and technical data to help the aviation community further define performance standards for “sense and avoid”.

Impact
Through the algorithm-development efforts, computer simulations, and flight evaluations, technical and operational issues will be explored and data generated that can help inform policy decisions in the aviation community, mature “sense and avoid” concepts and architectures, and help accelerate the development of the appropriate technology to enable the integration of UAS into civil airspace.

Public Release No: 12-1107

Problem
There is a need for Unmanned Aircraft Systems (UAS) to routinely operation in non-segregated civil airspace without degrading safety and without seriously disrupting legacy airspace users. Ground-based Sense and Avoid (GBSAA) is one potential mechanism to mitigation for the lack of see and avoid by providing traffic situation awareness in the pilot in the control station.

Objectives
The primary research goals are a set of core requirements for a GBSAA traffic display and alaboratory-tested concept of operations for its use. The project will create and evaluate candidatetraffic displays using operational and interactive tools based on the most promising and relevant ideasfrom ongoing research combined with existing cockpit displays of traffic information. The focus of the research is on the human machine interaction, specifically the methodologies of displaying traffic situation awareness and conflict information to a UAS pilot to help mitigate conflicts.

Activities
A distributable, online, low-fidelity test environment was created and access provided to a number of UAS pilots and other aviation subject matter experts. This will ideally provide a larger number of subjects to participate in the initial evaluation of the display usability for remaining well clear of other aircraft. Phase 2 of the evaluation will build upon the initial set of results and include additional perspective studies and updates to the early concepts.

Impact
GBSAA and other self-separation techniques will be pivotal in providing safe access to civil airspace for multiple purposes. By investigating and evaluating how the information is displayed, we could enable the UAS community to achieve this goal in an expedient manner, benefiting the UAS community, and potentially leading to a safe proliferation of civil and commercial UAS applications. The goal is to integrate the findings of this research in activities at MITRE supporting Air Force evaluation and implementation of GBSAA at several test locations. Work program funding has been received for additional studies and data analysis. Another goal of this research is to develop a framework for future distributed evaluations using a low-fidelity test environment.

Public Release No: 12-1085

Problem
The current suite of tools used by CAASD for air traffic analysis includes airspaceAnalyzer working in conjunction with commercial optimization technology (CPLEX). This technology is expensive and requires per-seat licensing, limiting the scope and scale of its use. Further, there are limitations to the scalability of commercial software to very large problems. As a result, airspaceAnalyzer can currently run a simulation of an air route traffic control center in a little faster than real time. In contrast, executing in parallel on a computational cluster would enable airspaceAnalyzer to simulate the airspace of the entire NAS in real time.

Objectives
Our goal is to demonstrate the use of airspaceAnalyzer for the entire NAS. We have identified three places where high performance computing can improve the tool's performance. Our idea is to deploy the computationally expensive portions of airspaceAnalyzer to a high performance cluster, enabled with multiple graphical processing units (GPUs) programmed as general-purpose computing devices. These improvements will be engendered by coarse-, medium-, and fine-grained parallelization, described below.

Activities
There are three levels at which we will improve the linear programming (LP) component of airspaceAnalyzer: coarse-, medium- and fine-grained parallelization. We have demonstrated results for each of these levels, with our final goal being integration of the three approaches into a single high performance system. At a coarse-grained level, many independent airspaceAnalyzer runs can be made concurrently, enabling simplified management of many runs. At a medium-grained level, a single very large LP computation can be distributed across many processors. This allows the creation of smaller and smaller (fine-grained) sub-problems which can be processed in parallel, much more efficiently that the single large problem.

Impact
This research should enable airspaceAnalyzer to simulate the full en route NAS in real time. This will significantly broaden the breadth and depth of MITRE's airspace analysis capability. In addition, the domain decomposition and computational methods developed in the course of this project have yielded unique visualizations of en route aircraft and their relationships, lending insight into the makeup, analysis, and efficient management of air traffic. Furthermore, the potential applications for this linear inequality solving technique extend beyond air traffic control to other domains which use systems of linear inequalities, ranging from optimization in operations research to physics and engineering.

Public Release No: 12-1257

Problem
The National Airspace System (NAS) in the United States is vulnerable to high risk events involving terrorist or other illegal activity, the unsafe operation of aircraft, or flights conducted in noncompliance with federal regulations. Because information to respond to and mitigate these events is collected mostly through manual processes, valuable time is often lost, or decisions are made absent pertinent details. MITRE is in a unique position to help because it has access to many sources of data, as well as substantial experience with data mining across government organizational boundaries. In addition, MITRE has access to significant operational expertise through its employees, customers, and relationships with educational institutions and private industry.

Objectives
The goal of this project is to develop a capability to produce actionable information for aviation stakeholders for use in identifying high risk aviation events in a timely manner. This project will prototype a capability to sift through the available data, highlighting significant or high risk events and potential precursors thereof. This information could be safety- or security-related and could help identify criminal activity, potential acts of terrorism, or flights not otherwise operating in compliance with applicable regulations. The insight gained from this could result in the discovery of information not previously explored, or could reveal “loopholes” in the current system, which, if closed, could result in a safer more secure aviation environment.

Activities
The project will accomplish its goals by first developing scenarios that could result in elevated risk to the NAS. Unclassified data from diverse sources (air traffic, security, law enforcement, compliance, etc. ) known to be relevant to these scenarios will be identified and explored. A federated database will be developed and data mining and data fusion techniques will be employed. Sensitive or classified data may be examined to determine if they would contribute to the accuracy of the prediction of a high risk aviation event. Stakeholders will be engaged to validate and refine scenarios and confirm that information outputs provide value in identifying and mitigating high risk events. The team will work closely with other MITRE projects to identify linkages, leverage resources, and maximize work products.

Impact
If successful, this research will result in a capability for use by government sponsors and stakeholders to gain access to information not previously available for identifying high risk aviation events. The users will have more timely access to high quality, reliable information compared to today’s manual processes. This should result in government cost savings by reducing false alarm events (e.g. unnecessary military scrambles) as well as cost savings to users by reducing delays and interruptions to operations. It also offers the hope of preventing loss of life due to acts of terrorism (or government response to or mitigation of those acts.) This capability could also serve as the basis for developing future risk assessment capabilities and decision support systems.

Public Release No: 12-0997

Problem
Today unmanned aircraft systems (UAS) can access the NAS through waivers from the FAA, although they do so under operational restrictions. These waivers are used by the Department of Defense (DOD), Department of Homeland Security (DHS), National Aeronautics and Space Administration (NASA), law enforcement agencies, and research institutions to conduct unmanned flights in non-segregated civil airspace. As an increasing number of UAS access the National Airspace System (NAS), their impact on air traffic controllers increases. This project is focused on understanding the air traffic control human factors implications when UAS operate in non-segregated civilian airspace.

Objectives
The objective of this research is to provide empirical data to support policy and procedure development by the FAA for supporting UAS operations. The research is also helping to identify areas that are not yet well understood and need further research to support more extensive UAS integration.

Activities
The project has included analyses of current operations as well as human-in-the-loop simulations. Current operations were explored through facility visits and analysis of radar and voice data which creates a picture of what is happening in the NAS today and where problem areas may exist. Human-in-the-loop simulations allow specific problems and potential solutions to be analyzed more closely through experimental manipulation.

Impact
This research is expected to bring awareness to some of the challenges air traffic controllers face with UAS operations, highlighting some of the issues that should be addressed prior to routine UAS operations in the NAS while guiding stakeholders toward potential solutions. This research is also supporting the development of the FAA's mid-term UAS Concept of Operation for UAS in the NAS.

Public Release No: 12-0319

Problem
The routine integration of unmanned aircraft into non-segregated civil airspace is important to enable a number of current and proposed applications ranging from military and homeland security to a wide variety of research and eventually commercial purposes. The Federal Aviation Administration (FAA) is currently undertaking a comprehensive overhaul of the National Airspace System (NAS) known as Next Generation Air Transportation System (NextGen). NextGen will include increased automation systems for both terminal and en-route Air Traffic Control (ATC). Improvements in two-way data communication links between aircraft and ATC will facilitate the use of such automation systems. The robust integration of unmanned aircraft systems (UAS) into NextGen automation systems is an integral component to meeting the far-term (2018+) NextGen vision. By determining how UAS flight operations and protocols may differ from traditional manned aircraft, informed decisions can be made concerning the data and interfaces required to incorporate UAS into NextGen automation systems, ultimately leading to safer and more efficient integration of UAS into non-segregated civil airspace.

Objectives
Through a variety of use cases, this research explored a number of alternative interfaces and data exchanges leveraging a standardized two-way communication link between NextGen systems and UAS. The objective of this research was to 1) demonstrate the potential operational benefit of increased machine-to-machine data communications that enhances both situation awareness and overall system effectiveness and 2) identify relevant data items, potential interfaces, and automation enhancements that would be needed. A flexible research platform that emulates proposed future systems and communication standards and architectures was designed to inform this research and enhance simulation capabilities. Real-time aircraft state and intent information as well as event driven messages such as amended flight plans and updated contingency routes are being investigated as data items that could aid integration if shared between UAS and NextGen systems.

Activities
To enable the data exchange research, the project team has implemented software emulations of the STANAG 4586 protocol and a generic UAS Ground Control Station(GCS). A customizable two-way data interface was implemented that allows various data schemas, messaging protocols, and communication interfaces and procedures to be researched and simulated in real time. Existing aircraft simulation capabilities were also enhanced to handle the unique aircraft performance characteristics and mission flight paths of unmanned aircraft. The integration of the flexible communication architecture into the various UAS and NextGen system emulation components (e.g., En Route Automation Modernization - ERAM) has provided an advanced end-to-end research platform capable of supporting real-time human-in-the-loop (HITL) experiments involving both manned and unmanned aircraft. The research platform was fully integrated into CAASD’s Aviation Integration, Demonstration and Experimentation for Aeronautics (IDEA) Lab.

Impact
This research has laid important ground work for communicating and demonstrating the importance of UAS data integration into NextGen systems. Through this research a reusable UAS simulation asset was created, which can be leveraged across multiple projects to investigate various levels of data interaction between UAS and NextGen systems. This will inform other ongoing research efforts and help MITRE explore future airspace integration operational concepts. The research platform developed has positioned MITRE to perform research that can inform the FAA’s plans for defining and upgrading proposed data interfaces, communication protocols, and future requirements for NextGen automation systems. It can also be used as a collaborative tool to engage military and other UAS stakeholders to help shape the shared vision of UAS system integration into the NAS.

Public Release No: 12-1235

Problem
NextGen enabling technologies may allow the FAA to relax current constraints, gaining capacity in IMC and allowing operations from runways with reduced separations. Such changes to the National Airspace System (NAS) require the appropriate safety assessment consistent with orders, guidelines and best practices of a safety management system. Analysts and proponents require tools to effectively and efficiently demonstrate the safety of NextGen concepts to regulatory authorities. This project involves the development of a set of modeling and simulation tools that can be used to assess the safety of NextGen concepts quantitatively. The analytical tools will be designed to evaluate proposed NextGen separation standards and to assist in the development of standards. Rather than using aircraft performance specifications, the models will be driven by actual observed data to the extent possible. The application will be modular to allow evolution with changing requirements.

Objectives
The ultimate goal is to enable analysts to assess the risk associated with NextGen operational changes, using a set of modeling and simulation tools that are data-driven, modular, and compatible with the SMS. An early objective is to develop a fast-time simulation model to determine levels of safety associated with NextGen runway operational concepts for parallel as well as non-parallel runway geometries. The model should support decisions by facilitating what-if scenarios, the incorporation of controller mitigations, and sensitivity analysis. Another objective is the inclusion of these capabilities in a graphical user interface (GUI) that facilitates an end-to-end analytic process without needing an external data analysis tool. A long-term stretch goal is to be able to model and assess procedures beyond the near-runway environment. This would require the ability to accurately model and characterize the path-keeping performance of more complex curved, climbing, and descending procedures.

Activities
An enabling activity is the development of trajectory models that are useful in a Monte Carlo simulation for the purposes of risk analysis. Rather than using static aircraft performance specifications, the trajectory models will be driven by empirical data. A hybrid of statistical modeling methods is used to create realistic synthetic position models from the original source data, which in turn are used by the simulation to introduce departure and arrival trajectories. These synthetic trajectories convey all the variability observed in the source data. Aircraft separations are recorded for further analysis, as are wake vortex encounters and their corresponding strengths. 2011 research focused on the development of the trajectory models described above, and the data collection and preparation that could make this possible. CAASD maintains a large repository of radar track data, accessible through Hadoop, which operates in a MapReduce software framework. Initial work began on a user interface that will allow non-programmers to interact with the model. FY12 efforts will focus on application of the model to the safety assessment of several CSPO concepts that introduce reduced spacing and may affect the potential for wake vortex encounters.

Impact
Demonstrating that safety requirements will be satisfied while implementing reduced runway separation standards will build stakeholder consensus and demonstrate our expertise in this area. This project should meaningfully contribute to the ability of FAA to implement the NextGen Implementation Plan, primarily in the High Density Airports solution set and the Closely Spaced Parallel, Converging and Intersecting Runway Operations portfolio.

Public Release No: 12-0366

Problem
Environmental concerns pose significant constraints to sustainable growth for aviation and need to be addressed when assessing NextGen operational improvements. The FAA's Office of Environment and Energy is leading a large effort to integrate and improve upon legacy emissions and noise modeling methodologies through the Aviation Environmental Design Tool (AEDT). AEDT is intended to be the next environmental regulatory compliance model and will provide both noise and emissions modeling capabilities. This research project is focused on developing linkages between MITRE/CAASD's systemwideModeler and AEDT to enable a NAS-wide assessment of tradeoffs among operational and environmental performance goals. systemwideModeler has been exercised for assessing NextGen operational improvements using performance metrics such as delay, system throughput, and controller workload. This project will expand the NextGen benefits metrics portfolio to include environmental metrics thereby making it increasingly relevant to a broader group of stakeholders.

Objectives
This work focuses on linking MITRE/CAASD's systemwideModeler with the FAA’s AEDT to enable estimation of fuel burn, emissions, and noise impacts of NextGen operational improvements at the NAS-wide scale. NextGen operational improvements of particular interest in terms of environmental performance include but are not limited to improvements focused on increased use of Performance Based Navigation (PBN), improved multiple runway operations, separation management, improved approaches, etc.

Activities
Planned activities for this project include further refinement of methods for translating systemwideModeler results into appropriate trajectory inputs for AEDT environmental modeling purposes and a sample analysis to demonstrate the capability developed. More specifically, this work includes testing, implementation, and refinement of trajectory modification approaches that enhance the resolution for terminal area trajectories from systemwideModeler and introduce delay maneuvers such as vectors or holds to accommodate delay redicted by systemwideModeler.

Impact
Firstly, this work expands the NextGen benefits portfolio to include environmental performance metrics such as fuel burn, emissions, and noise. Secondly, this work establishes connectivity with AEDT, which is intended to replace legacy noise and emissions modeling tools thereby advancing noise and emissions modeling capabilities within MITRE/CAASD. Finally, this work aligns with the internal FAA-AEE mandate to establish connectivity with operational modeling tools such as the Airspace Concepts Evaluation System (ACES), the Airport and Airspace Simulation Model (SIMMOD), and the System Wide Analysis Capability (SWAC). This is an area where MITRE/CAASD with expertise in the air traffic simulation domain can make significant contributions to enabling a comprehensive NextGen analysis that assesses tradeoffs among operational and environmental considerations.

Public Release No: 12-0438

Problem
To make the UAS sense and avoid capability robust to command and control communication link interruptions, an aircraft-mounted solution to the sense and avoid problem is strongly desired. Thus, in the event of a link dropout, the UAS will have a self-contained capability to sense and avoid possible collisions. Recent advances in radar exploitation make it feasible to consider building onboard collision-avoidance radars utilizing very small antennas and shifting the burden away from the radar frontend and into digital processing hardware.

Objectives
This project is exploring the possibility that the use of bi-static radar technology will allow the development of the “sense” portion of this sense-and-avoid capability. Research success would lead to systems that are smaller and less costly than what’s possible today, and thus readily deployable to a large number of UAS, of various groups. Another potential advantage of bistatic receivers is that they may be able to make use of existing surveillance radar signals, in addition to making more efficient use of additional illuminators that are added on an as-needed basis. We will investigate the feasibility, performance, and cost of this potential class of solutions by creating a set of variants of some basic designs of the type mentioned above. These concepts will be compared and contrasted in terms of performance against cost, processing complexity, weight, power, and other factors.

Activities
In the first phase of the study we will establish the signal processing techniques that will allow for the accurate detection and estimation of target location and bearing. Having established the baseline algorithm, we will then examine performance against radar product quality, paying particular attention to detection range, bandwidth, and update rate. This will allow the development of sensor requirements, in terms of the transmitter, receiver, and onboard processing capability. We will then be able to develop illumination requirements on the radar system. Based on these requirements we will create several basic designs involving combinations of mono- and bi-static elements, both groundbased and on-board the platforms. As part of the design of bistatic solutions, we will consider a variety of existing, planned, and potential illumination sources, as well as solutions for a variety of UAS size classes

Impact
This approach may provide a cost-effective solution for an on-board non-cooperative sensor for UAS a sense and avoid capability.

Public Release No: 12-1171

Problem
Ideally, the number of arrivals a terminal can handle during a time period—given conditions at the airport such as weather, infrastructure availability, and traffic volume—would match the actual number of inbound arrivals. Mismatches between these rates often lead to serious problems. If too many arrivals actually enter the terminal, controllers will often need to use less efficient maneuvers such as vectoring to maintain needed spacing. Controllers may also need to resort to aircraft holding in both terminal and en route airspace until traffic congestion in terminal airspace dissipates. Holding results in excessive fuel burn, delays, and schedule disruptions. If too few arrivals actually enter the terminal, valuable airport landing capacity goes unused. In today’s operations, mismatches between the planned and actual arrival rates often occur gradually and are not even noticed—until it is too late and the problems just described materialize and become severe.

Objectives
The objective of this project is to develop a mechanism that will alert traffic managers to the need for them to consider taking action to adjust the rate of aircraft being delivered into terminal airspace. The mechanism will be based on identifying a set of real-time ‘signal’ metrics that reflect actual operations in the terminal. The signal metrics will be used to objectively anticipate or predict the likelihood of problems developing in the terminal before those problems actually occur and become severe, and equally important, with enough lead time for traffic managers to take appropriate mitigating actions, such as incrementally adjusting the delivery rate of aircraft into the terminal. An important success criterion for this project is that the signal metrics provide an objective basis for controllers to take action early and reduce reliance upon manual monitoring of the terminal operations.

Activities
Our approach to this problem has been to identify two sets of signal metrics: one that can be used to gauge the state of operations throughout the day, and another set to gauge TRACON overload. In consultation with subject matter experts, we identified an initial set of signal metrics, including aircraft counts, length of final approach, and airborne holding. Through analysis of historical traffic, we are examining correlations between the sets of metrics to determine how well they work as predictors of impending overload, and with what look-ahead time. Later, using fast-time simulation, we plan to validate that the early implementation of mitigating actions based on signal metrics will increase the efficiency of operations and reduce the impact to the environment.

Impact
This effort will provide an alerting technology that will enable decision making to be based on quantitative analysis instead of on subjective assessment. The alerting technology will facilitate the timely and incremental adjustments of arrival rates and thereby support the NextGen goals to increase the predictability and efficiency of operations and reduce the impact to the environment. In addition, the signal metrics will enable improved collaboration among service providers across facilities as well as with NAS users.

Public Release No: 12-0699

Problem
In today’s operations, poor situational awareness and a lack of efficient information tools, such as integrated traffic, weather, and airspace resource information, can result in traffic managers reacting to disruptions instead of proactively planning. Coordination between facilities is made difficult when not all facilities are aware of the state of departure routes, or when some do not understand the operational concerns or airspace constraints within another facility’s boundaries. When decisions are made without sufficient coordination or supporting data the result can be unintended disruptions to the NAS and delayed flights.

Objectives
The High Density Area Departure/Arrival Management (HDDAM) concept attempts to solve this problem with the philosophy that decisions should be assigned to those who are already performing other tasks with the relevant data and will be most directly affected by the consequences of those decisions. The HDDAM concept realigns the focus of decision making and actions between the ARTCC, TRACON, and Tower into roles and responsibilities that encourage proactive planning and decision making within the NAS. The objective of this research is to create an experimental environment in which the redistribution of the roles and responsibilities among the decision makers in different facilities can be demonstrated and evaluated. In this environment decision makers will be empowered with the right decision support tools and share integrated traffic, weather, and airspace resource information. The research will include a comparison with and evaluation of similar activities done under today’s operating paradigm.

Activities
This project is undertaking a number of activities to demonstrate that the HDDAM concept is a viable and a credible possible future. These include identifying the capabilities needed to support the concepts, prototyping the needed capabilities, performing human-in-the-loop experiments to demonstrate and evaluate the concepts, and publishing the results of all research associated with the HDDAM concept.

Impact
The successful completion of the development and evaluation of the HDDAM concept will produce an operational concept along with metrics from experimental evaluation and stakeholder demonstration and feedback. These results should inform future architectural decisions for a number of NextGen decision support environments. Experimental results to date suggest that implementation of the HDDAM concepts could accelerate the implementation of NextGen solution sets, including reducing weather impact, increasing arrival and departures at high density airports, increasing flexibility in the terminal environment, and improving collaborative air traffic management.

Public Release No: 12-0705

Problem
Strategic planning under uncertainty is a challenge constantly faced in today’s traffic management system. Hours before a predicted weather event, plans must be made to balance the predicted demand with predicted airspace capacity, potentially reduced due to weather or other constraints, all the while maintaining safe operating conditions and minimizing system disruptions. What makes this problem so challenging is that the uncertainties in both the weather and traffic demand predictions that exist at longer planning horizons (generally greater than 2 hours in advance) are significant. Currently there are limited decision support capabilities that can simulate and evaluate the impact of potential plans.

Objectives
The goal of the Flow Contingency Management (FCM) research is to address these challenges by providing a more scientific approach to strategic traffic flow management (TFM). Specifically, we are developing quantitative methodologies that directly capture the uncertainties present at these planning horizons. This requires that we develop models that can predict demand at longer time horizons, and predict nominal and weather-impacted capacity effects. The integrated simulation models resulting from this research will provide decision makers with the capability to analyze predicted problems well in advance of their development, and to determine suitable plans to mitigate the congestion. To manage competing objectives effectively, FCM will incorporate a formal risk management approach to both negotiate the plans associated with multiple outcomes and the costs and risks of implementation in an uncertain environment. In essence, FCM aims to construct a solvable problem in the future by defining the system constraints necessary to do so, assessing degrees of freedom for creating a course of action, and building a mitigation plan while deferring the details until the situation evolves.

Activities
In pursuit of a scientific approach to strategic TFM, FCM will develop formal analysis methods that capture the inherent traffic and weather uncertainty and quantify how these impacts will effect TFM operations. Through a collaborative effort with professors at the University of North Texas and Washington State University, we will leverage control theory models to develop simulations of TFM-impacts induced from predicted weather. This research entails the development of weather-impact models that translate probabilistic weather forecasts into trajectories of TFM-impact with associated statistics of likelihood. In addition, aggregate day-of demand forecasts will be derived to predict the location and volume of traffic. Combining these estimates within a stochastic queuing model, we can quantify the delays associated with the weather-impacted resources, nominal operating constraints, as well as the traffic management initiatives (TMIs) imposed. Furthermore, by utilizing such a comprehensive model, we can employ sensitivity analysis methods and heuristic optimization approaches to aid in the design of the TMI parameters, effectively defining an integrated solution to strategic traffic flow management problems.

Impact
The FCM concept proposed aims to alleviate the deficiencies in the current system by improving the information provided to decision makers and incorporating this information into a decision support tool that can simulate and evaluate the resulting actions suggested to alleviate the potential future congestion. In addition to the capability to design strategic plans, the concept incorporates simulation capabilities for decision makers, enabling multiple levels of interaction. Finally, given that FCM operates in a computerized decision support environment, improved simulation awareness will be available to all relevant stakeholders and decisions made in this environment can be automatically relayed. By addressing the current deficiencies using novel, quantitative methods, it is envisioned that unnecessary delays could be avoided, which would improve system efficiency and benefit all NAS stakeholders.

Public Release No: 12-1164

Problem
Models that simulate the entire National Airspace System (NAS), such as MITRE’s systemwideModeler, are used to estimate the network and aggregate effects of changes to the NAS. Typically, the analyses conducted with systemwideModeler serve to estimate the future delay savings likely to result from some package of NextGen improvements. Decisions about how to invest limited funds have grown more complex, requiring an understanding of dependencies between improvements in a package and an ability to assess trade-offs between different investment choices. To address the complexity of these decisions, system-wide modeling tools must be able to answer a much broader range of questions.

Objectives
This research seeks to develop NAS-wide modeling capabilities that can be used to inform investment decision making. The process of building scenarios, executing simulations, and conducting analyses must be agile, and the results generated from these analyses must satisfy the needs of all stakeholders involved in the decision-making process. Recent advances in multi-core and distributed computer architectures – such as the MITRE Elastic Goal-Directed (MEG) Simulation Framework – have made it possible to execute huge ensembles of simulations, thereby enabling the exploration of a large number of potential outcomes. In addition, this ensemble approach allows for the inclusion of additional metrics, such as the predictability of on-time performance. This approach to NextGen problem analysis can generate a repository of model results that will facilitate agile response to pressing questions as they arise.

Activities
This research is conducting a number of independent analyses using systemwideModeler and the MEG simulation framework. For example, a Monte Carlo simulation is being conducted to randomly introduce delays from external causes (e.g. baggage handling, crew constraints, deicing, etc.). The goal is to understand the dynamics of the schedule “padding” required to achieve on-time performance targets. This research will capture the network effects of these delays and show how the costs of schedule “padding” to users will be affected by changes in predictability. A second study will analyze the degree to which intra-day variation affects the validity of aggregating results across a sample of “representative” days. This research will lay a foundation for future analyses by identifying combinations of sample day sizes and simulations within a given sample day that will produce NAS-wide results that meet a specified level of accuracy. Another analysis will employ a genetic algorithm to understand the role that arrival reservoirs should play at various airports in a trajectory-based operations environment. A fourth analysis will use the rerouting capabilities of systemwideModeler to explain how weather forecast accuracy affects the benefits of various traffic flow management initiatives.

Impact
This research is intended to position MITRE's NAS-wide modeling and simulation tools to be able to provide decision makers with analytic support to inform investment decisions. This will enable decision makers to better understand the impact of their decisions on the performance of the NAS as a whole, and will help them target investments to those areas most in need of performance improvement. The more comprehensive set of metrics resulting from this research will enable a more informed engagement by stakeholders in the advancement of NextGen.

Public Release No: 12-0532

Problem
Aircraft naturally generate air turbulence patterns known as wakes, which can be hazardous to trailing aircraft, particularly for operations on airport approach and departure where aircraft are often closely spaced. As NextGen concepts move toward increasing en route and terminal throughput, wake turbulence separation may become a limiting factor in the pursuit of capacity improvements. Better knowledge of the probable location of wakes (for air traffic controllers as well as pilots) could help provide safe separation from wake turbulence while avoiding unnecessary restrictions to operations. Using available data on temperature profile, eddy dissipation rate, and wind fields, algorithms are currently available to reliably estimate wake characteristics. Such a capability can be used to drive displays of wake information on air traffic control workstations and pilot cockpit displays of traffic information, and thereby improve situational awareness, safety, and capacity.

Objectives
Research is continuing in FY12, with a focus on expanding the benefits analysis to incorporate results from both of the MITRE developed airport capacity and delay models (runwaySimulator and systemwideModeler). These models will be used to estimate the capacity and delay impact of using these wake displays in selected NextGen operations at a broad range of airports. Improvements to the wake displays identified during the FY11 human in the loop simulations will be implemented and used in demonstrations and discussions with interested FAA and aviation industry stakeholders.

Activities
Improvements to the wake models developed in FY11 will be researched in order to further research the impacts of uncertainty of wake behavior. Transition activities are of high importance during FY12, as this is the third year of the MOIE, and any additional analysis, simulation, or modeling that can be done to assist with the transition activites will be undertaken.

Impact
This work supports the operational feasibility and acceptance of multiple NextGen operational improvements. If NextGen separation reductions result in an unacceptable increase in wake encounters, or worse, the first wake accident in U.S. history (when IMC separations and procedures are observed), then the acceptance of these NextGen concepts will be limited and the investment in them wasted. Providing additional wake planning, situational awareness, and warning information to pilots and controllers will help enable the safe application of reduced separations, when possible. Additionally, the prototype wake displays developed for the controller workstation and Boeing 777 cockpit simulator will add to the suite of capabilities that can be leveraged by other projects. These prototypes can also be leveraged to support an RTCA activity that is developing concept and standard documents for the provision, broadcast, and use of airborne wind and wake parameter measurements. MITRE co-chairs the RTCA working group that is developing these concepts and standards.

Public Release No: 12-0744