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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Emergency Preparedness for Catastrophic Events at Small and Medium Sized Airports: Lacking or Not?

The implementation of security methods and processes in general has had a decisive impact on the aviation industry. However, efforts to effectively coordinate varied aspects of security protocols between agencies and general aviation components have not been adequately addressed. Whether or not overall security issues, especially with regard to planning for catastrophic terrorist events, have been neglected at the nation's smaller airports is the main topic of this paper. For perspective, the term general aviation is generally accepted to include all flying except for military and scheduled airline operations. Genera aviation makes up more than 1 percent of the U.S. Gross Domestic Product and supports almost 1.3 mission high-skilled jobs in professional services and manufacturing and hence is an important component of the aviation industry (AOPA, n.d.). In both conceptual and practical terms, this paper argues for the proactive management of security planning and repeated security awareness training from both an individual and an organizational perspective within the general aviation venue. The results of a research project incorporating survey data from general aviation and small commercial airport managers as well as Transportation Security Administration (TSA) employees are reported. Survey findings suggest that miscommunication does take place on different organizational levels and that between TSA employees and airport management interaction can be contentious and cooperation diminished. The importance of organizational training for decreasing conflict and increasing security and preparedness is discussed as a primary implication.

Sweet, Kathleen M.↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
DOE OSTI2025

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗
NASA NTRS2003

Investigating the Partitioning of Inorganic Elements Consumed by Humans between the Various Fractions of Human Wastes: An Alternative Approach

The elemental composition of food consumed by astronauts is well defined. The major elements carbon, hydrogen, oxygen, nitrogen and sulfur are taken up in large amounts and these are often associated with the organic fraction (carbohydrates, proteins, fats etc) of human tissue. On the other hand, a number of the elements are located in the extracellular fluids and can be accounted for in the liquid and solid waste fraction of humans. These elements fall into three major categories - cationic macroelements (e.g. Ca, K, Na, Mg and Si), anionic macroelements (e.g. P, S and Cl and 17 essential microelements, (e.g. Fe, Mn, Cr, Co, Cu, Zn, Se and Sr). When provided in the recommended concentrations to an adult healthy human, these elements should not normally accumulate in humans and will eventually be excreted in the different human wastes. Knowledge of the partitioning of these elements between the different human waste fractions is important in understanding (a) developing waste separation technologies, (b) decision-making on how these elements can be recovered for reuse in space habitats, and (c) to developing the processors for waste management. Though considerable literature exists on these elements, there is a lack of understanding and often conflicting data. Two major reasons for these problems include the lack of controlled experimental protocols and the inherently large variations between human subjects (Parker and Gallagher, 1988). We have used the existing knowledge of human nutrition and waste from the available literature and NASA documentation to build towards a consensus to typify and chemically characterize the various human wastes. It is our belief, that this could be a building block towards integrating a human life support and waste processing in a closed system.

Wignarajah, Kanapathipillai↗
NASA NTRS2020

Predicting Gate Conflicts at Charlotte Douglas International Airport Using NASA ATD-2 Fused Data Sources

NASA is conducting the Airspace Technology Demonstration-2 to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system. The IADS system is powered by real-time System Wide Information Management feeds which provide an accurate and high fidelity view of the lifecycle of a flight. This data can be leveraged to drive efficiencies in the National Airspace System. For non safety critical applications there is opportunity for third party service providers to offer this type of data-driven prediction service in near real-time. This paper investigates the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach. We quantify our predictive accuracy and identify paths for improvement. Through this iterative process we hope to evolve our models and methods to a near real-time prediction service.

Airspace Technology Demonstration 2↗
NASA NTRS1989

Approach to transaction management for Space Station Freedom

An approach to managing the operations of the Space Station Freedom based on their external effects is described. It is assumed that there is a conflict-free schedule that, if followed, will allow only appropriate operations to occur. The problem is then reduced to that of ensuring that the operations initiated are within the limits allowed by the schedule, or that the external effects of such operations are within those allowed by the schedule. The main features of the currently adopted transaction management approach are discussed.

Easton, C. R.↗
NASA NTRS2002

Use of Traffic Intent Information by Autonomous Aircraft in Constrained Operations

This paper presents findings of a research study designed to provide insight into the issue of intent information exchange in constrained en-route air-traffic operations and its effect on pilot decision-making and flight performance. The piloted simulation was conducted in the Air Traffic Operations Laboratory at the NASA Langley Research Center. Two operational modes for autonomous flight management were compared under conditions of low and high operational complexity (traffic and airspace hazard density). The tactical mode was characterized primarily by the use of traffic state data for conflict detection and resolution and a manual approach to meeting operational constraints. The strategic mode involved the combined use of traffic state and intent information, provided the pilot an additional level of alerting, and allowed an automated approach to meeting operational constraints. Operational constraints applied in the experiment included separation assurance, schedule adherence, airspace hazard avoidance, flight efficiency, and passenger comfort. The strategic operational mode was found to be effective in reducing unnecessary maneuvering in conflict situations where the intruder's intended maneuvers would resolve the conflict. Conditions of high operational complexity and vertical maneuvering resulted in increased proliferation of conflicts, but both operational modes exhibited characteristics of stability based on observed conflict proliferation rates of less than 30 percent. Scenario case studies illustrated the need for maneuver flight restrictions to prevent the creation of new conflicts through maneuvering and the need for an improved user interface design that appropriately focuses the pilot's attention on conflict prevention information. Pilot real-time assessment of maximum workload indicated minimal sensitivity to operational complexity, providing further evidence that pilot workload is not the limiting factor for feasibility of an en-route distributed traffic management system, even under highly constrained conditions.

Wing, David J.↗
NASA NTRSDate not supplied

Urban Air Mobility Traffic Analysis Tool for Airspace Involving Vertiport Hub Operations to Examine Simulated Conflicts

Presented herein are details of the development of an Urban Air Mobility Traffic Analysis Tool (UAM TAT), designed to analyze the traffic of Vertical Take-off and Landing (VTOL) air-craft in urban agglomerations to enable the Urban Air Mobility(UAM) operations. The tool aims to address challenges of UAM operations in a simulated airspace by testing different scenarios and methods for air traffic management. It emphasizes the need for effective risk management practices and methodologies to ensure the safety of operations, given the increasing use of highly automated aircraft (i.e., Advanced Air Mobility (AAM))in urban environments. The tool is modular and allows for the simulation of various families of VTOLs and different air traffic rules, making it suitable for defining and estimating airspace and vertiports capacity, determining VTOL requirements, and testing and comparing air traffic management methods. The results of five different simulations are presented that show the capabilities and functionalities of the tool. The simulations present the impact of vertiport hub operations and various ATM approach on the VTOLs’ conflicts.

Urban Air Mobility↗
NASA NTRS2002

Regaining Lost Separation in a Piloted Simulation of Autonomous Aircraft Operations

NASA is currently investigating a new concept of operations for the National Airspace System, designed to improve capacity while maintaining or improving current levels of safety. This concept, known as Distributed Air/Ground Traffic Management (DAG-TM), allows appropriately equipped autonomous aircraft to maneuver freely for flight optimization while resolving conflicts with other traffic and staying out of special use airspace and hazardous weather. While Airborne Separation Assurance System (ASAS) tools would normally allow pilots to resolve conflicts before they become hazardous, evaluation of system performance in sudden, near-term conflicts is needed in order to determine concept feasibility. If an acceptable safety level can be demonstrated in these situations, then operations may be conducted with lower separation minimums. An experiment was conducted in NASA Langley s Air Traffic Operations Lab to address issues associated with resolving near-term conflicts and the potential use of lower separation minimums. Sixteen commercial airline pilots flew a total of 32 traffic scenarios that required them to use prototype ASAS tools to resolve close range pop-up conflicts. Required separation standards were set at either 3 or 5 NM lateral spacing, with 1000 ft vertical separation being used for both cases. Reducing the lateral separation from 5 to 3 NM did not appear to increase operational risk, as indicated by the proximity to the intruder aircraft. Pilots performed better when they followed tactical guidance cues provided by ASAS than when they didn't follow the guidance. As air-air separation concepts are evolved, further studies will consider integration issues between ASAS and existing Airborne Collision Avoidance Systems (ACAS).These types of non-normal events will require the ASAS to provide effective alerts and resolutions prior to the time that an Airborne Collision Avoidance System (ACAS) would give a Resolution Advisory (RA). When an RA is issued, a pilot must take immediate action in order to avoid a potential near miss. The Traffic Alert and Collision Avoidance System (TCAS) II currently functions as an ACAS aboard commercial aircraft. Depending on the own aircraft s altitude, TCAS only issues RA s 15-35 seconds prior to the Closest Point of Approach (CPA). Prior to an RA, DAG-TM pilots operating autonomous aircraft must rely solely on ASAS for resolution guidance. An additional area of DAG-TM concept feasibility relates to a potential reduction in separation standards. Lower separation standards are likely needed in order to improve NAS efficiency and capacity. Current separation minimums are based in large part on the capabilities of older radar systems. Safety assessments are needed to determine the feasibility of reduced separation minimums. They will give strong consideration to surveillance system performance, including accuracy, integrity, and availability. Candidate surveillance systems include Automatic Dependent Surveillance-Broadcast (ADS-B) and multi-lateration systems. Considering studies done for Reduced Vertical Separation Minimums (RVSM) operations, it is likely that flight technical errors will also be considered. In addition to a thorough evaluation of surveillance system performance, a potential decision to lower the separation standards should also take operational considerations into account. An ASAS Safety Assessment study identified improper maneuvering in response to a conflict (due to ambiguous or improper resolution commands or a pilot s failure to comply with the resolution) as a potential safety risk. If near-term conflicts with lower separation minimums were determined to be more challenging for pilots, the severity of these risks could be even greater.

Barhydt, Richard↗
NASA NTRS2007

In-Space Crew-Collaborative Task Scheduling

For all past and current human space missions, the final scheduling of tasks to be done in space has been devoid of crew control, flexibility, and insight. Ground controllers, with minimal input from the crew, schedule the tasks and uplink the timeline to the crew or uplink the command sequences to the hardware. Prior to the International Space Station (ISS), the crew could make requests about tomorrow s timeline, they could omit a task, or they could request that something in the timeline be delayed. This lack of control over one's own schedule has had negative consequences. There is anecdotal consensus among astronauts that control over their own schedules will mitigate the stresses of long duration missions. On ISS, a modicum of crew control is provided by the job jar. Ground controllers prepare a task list (a.k.a. "job jar") of non-conflicting tasks from which jobs can be chosen by the in space crew. Because there is little free time and few interesting non-conflicting activities, the task-list approach provides little relief from the tedium of being micro-managed by the timeline. Scheduling for space missions is a complex and laborious undertaking which usually requires a large cadre of trained specialists and suites of complex software tools. It is a giant leap from today s ground prepared timeline (with a job jar) to full crew control of the timeline. However, technological advances, currently in-work or proposed, make it reasonable to consider scheduling a collaborative effort by the ground-based teams and the in-space crew. Collaboration would allow the crew to make minor adjustments, add tasks according to their preferences, understand the reasons for the placement of tasks on the timeline, and provide them a sense of control. In foreseeable but extraordinary situations, such as a quick response to anomalies and extended or unexpected loss of signal, the crew should have the autonomous ability to make appropriate modifications to the timeline, extend the timeline, or even start over with a new timeline. The Vision for Space Exploration (VSE), currently being pursued by the National Aeronautics and Space Administration (NASA), will send humans to Mars in a few decades. Stresses on the human mind will be exacerbated by the longer durations and greater distances, and it will be imperative to implement stress-reducing innovations such as giving the crew control of their daily activities.

Jaap, John↗
NASA NTRS2010

The Monotonic Lagrangian Grid for Rapid Air-Traffic Evaluation

The Air Traffic Monotonic Lagrangian Grid (ATMLG) is presented as a tool to evaluate new air traffic system concepts. The model, based on an algorithm called the Monotonic Lagrangian Grid (MLG), can quickly sort, track, and update positions of many aircraft, both on the ground (at airports) and in the air. The underlying data structure is based on the MLG, which is used for sorting and ordering positions and other data needed to describe N moving bodies and their interactions. Aircraft that are close to each other in physical space are always near neighbors in the MLG data arrays, resulting in a fast nearest-neighbor interaction algorithm that scales as N. Recent upgrades to ATMLG include adding blank place-holders within the MLG data structure, which makes it possible to dynamically change the MLG size and also improves the quality of the MLG grid. Additional upgrades include adding FAA flight plan data, such as way-points and arrival and departure times from the Enhanced Traffic Management System (ETMS), and combining the MLG with the state-of-the-art strategic and tactical conflict detection and resolution algorithms from the NASA-developed Stratway software. In this paper, we present results from our early efforts to couple ATMLG with the Stratway software, and we demonstrate that it can be used to quickly simulate air traffic flow for a very large ETMS dataset.

Kaplan, Carolyn↗
NASA NTRS1998

Steps Towards the Integration of Conflict Resolution with Metering and Scheduling

NASA Ames Research Center is developing decision support tool technology for air traffic controllers to improve the efficiency and capacity of National Airspace System. The goal is to provide technology, tools and procedures that result in the highest possible level of user preferred trajectories whenever possible with safe and efficient traffic management when necessary. The work is being conducted under the NASA Advanced Air Transportation Technology Program in cooperation with the FAA through the Inter-Agency Integrated Product Team. The objective is to develop technology and procedures that lead towards a seamless integration of conflict resolution with metering and scheduling for arrival aircraft and en route aircraft that are under metering restrictions. A requirement is that the integration incorporate user preferred trajectories. The ultimate goal is the implementation and validation of the Descent Advisor (DA) concept which provides clearance advisories to a sector controller that simultaneously meet metering constraints, are conflict free, incorporate a user preferred (e.g., minimum fuel) descent profile, and generally require no further corrective clearance as the aircraft transitions from en route cruise into the TRACON. The DA concept may also be applied to en route aircraft under metering constraints, e.g., miles-in-trail. To achieve the DA concept a stepwise development and field evaluation is anticipated. This paper addresses the initial steps towards implementation of the DA. The Traffic Management Advisor (TMA) computes arrival time sequence and required delay information for display to the sector controller during periods when arrivals must be metered due to landing rate restrictions at the airport. The Initial Conflict Probe (ICP) compares trajectory predictions for all aircraft and alerts the controller when any two aircraft are predicted to violate separation standards (5 mi. and 2000 ft. in en route airspace). ICP also includes a trial planning function allowing the controller to develop and check a separate "what if" trajectory for conflict resolution. TMA and ICP currently operate independent of one another and have separate controller displays. The TMA meter list is on the radar controller's plan view display. The ICP is still under development, but the current concept calls for a list of predicted conflicts at the data controller position. The research described herein address two steps towards the implementation of DA. The first is to develop a concept for integrated display of conflict and metering information on a controller's display. The objective is to provide the controller with situational awareness of one problem while he develops a solution to the other. The next step is to expand the concept for display of automated clearance advisories for one problem (e.g., metering) which take into account the other problems (e.g., conflicts). Information to be communicated between TMA and ICP to facilitate' manual or automated advisories is being identified as the concept matures. In order to study the ICP/TMA integration concept the CTAS conflict probe capability has been adapted to Ft. Worth Center. The system is being validated in the laboratory with all-track data and for non -interference with TMA, TMA is already running as a daily use prototype at Ft. Worth Center. A laboratory prototype system has been developed under the CTAS baseline which combines conflict and metering information on a common user interface. Elements of the user interface are shown in Figure 1. In this simple illustration the user sees the simultaneous effect of a trial plan on meter fix delay and conflict status. Delay information is shown in the aircraft flight data block, the meter list, and the TMA timeline. Experience during Descent Advisor development and observations at Ft. Worth Center high and low attitude arrival sectors during metering suggests that manual trial planning will be unworkable during rush periods due to high controller workload.

McNally, B. David↗
NASA NTRS1997

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗
DOE OSTI2026

History of the Chemical Stockpile Emergency Preparedness Program – Volume 2: Lessons Learned (Final Report 2026)

The CSEP Program involved multiple federal agencies, multiple states, and numerous local units of government. The involvement of diverse agencies at different levels of government led to conflict and difficulty in resolving issues. That, in turn, led to slow progress in achieving program goals. Program management was criticized repeatedly by the U.S. General Accounting Office (GAO). After other mechanisms for interagency coordination had failed to give the desired results, FEMA and the Army agreed to form site-specific and national-level IPTs. The IPTs worked well to break the logjam and accelerate implementation of the program. Key elements of the IPTs’ success included constant interagency communication, solving problems at the lowest level, and obtaining buy-in from higher authority. The IPT technique could be applied to other emergency management and homeland security programs that require interagency coordination.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRSDate not supplied

A Flight Replanning Tool for Terminal Area Urban Air Mobility Operations

In this presentation we describe and evaluate a flight replanning tool, called the trial planner, for terminal area air transport applications. The trial planner employs predefined airspace structures to generate rerouting options between vertiports. In a simplified definition, vertiports are facilities that provide services for managing the take-off and landing of autonomous or manned electric vertical take-off or landing (eVTOL) aircraft. The airspace structures involved in our test cases are arrival routes that consist of predefined entry points to which a transitional path is computed dynamically from the current position of the aircraft. Rerouting options include the transitional path and approach segment along the arrival route; these rerouting options are not vetted for potential conflicts with other operations until a human operator commits to a choice and forwards the flight plan modification for approval from an automated airspace management service. A selected rerouting option is executed by loading a flight plan file to the aircraft ground control station. We conducted a qualitative evaluation of the trial planner using ratings provided by flight crews and air transportation human factors experts with relevant experience from air traffic control. Evaluation criteria included trust in the route recommendations and adequate explanation for the route options. We detail the logic and implementation of the trial planner, as well as report the results of the evaluation of the implementation herein.

Trial planning↗
NASA NTRS2015

Terminal Sequencing and Spacing (TSS)

The Federal Aviation Administration's (FAA) Next Generation Air Transportation System (or NextGen) is being designed to support the predicted increases in traffic volume and to increase the capacity, efficiency and safety of the National Airspace System (NAS). The Federal Aviation Administration (FAA) identifies Performance-Based Navigation (PBN) as a key enabling capability of NextGen and is actively publishing PBN procedures at major airports throughout the United States. Standard Terminal Arrival Routes (STARs), procedures, and approaches are designed to facilitate fuel-efficient continuous descent operations. However, their use is limited during periods of high traffic demand due to the complexity of merging multiple streams of aircraft to the same airport. As a result, most arrivals in the Terminal Radar Approach Control (TRACON) area continue to be controlled using radar vectoring and step-down descents, resulting in high workload for controllers and diverting aircraft from efficient PBN trajectories. To address this issue, NASA developed the Terminal Sequencing and Spacing (TSS) system, an advanced arrival management technology that combines time-based scheduling and controller-based precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals on Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. The two main components of TSS are: 1) a scheduler that de-conflicts merging arrivals in the terminal area by computing appropriate arrival times to the runway threshold and upstream terminal merge points, and 2) a set of Controller-Managed Spacing (CMS) decision support tools to efficiently assist schedule conformance. Sixteen high-fidelity human-in-the-loop simulations involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of-concept design to a fully functional prototype. Results indicate high controller use and acceptability of the CMS tools as well as improved PBN route conformance (Figure 2). The TSS technology was transferred to the FAA in 2014, and it is targeted for deployment to several busy airports in the U.S. starting in 2018. Potential enhancements to TSS using DataComm will also be presented.

controller-managed spacing↗