The optimal to initiate a conflict resolution maneuver has been cited as a tradeoff between trajectory efficiency and conflict detection/resolution certainty. One of the goals of this research is to add a new dimension to the tradeoff by considering controller workload as well. Maintaining acceptable levels of controller workload is often achieved through traffic flow management (TFM) restrictions, which cause not just the inefficiency of a single trajectory, but the trajectory inefficiency of entire traffic flows. It is hypothesized that by developing strategies that consider variable (rather than static) lookahead times for conflict detection and trial planning, controller productivity can be increased, which would result in increased sector capacities and reduce the need for TFM restrictions. When sector loading is high, the strategy should emphasize increased controller productivity. When sector loading is low to moderate, the focus can shift to strategies that emphasize single trajectory efficiency. The term effective trial planning lookahead time is introduced and determines the ability to capture secondary conflicts (conflicts that occur downstream of the initial conflict). A modeling study was conducted to compare different controller strategies based on lookahead times. Combining strategies can decrease the number of conflicts by over 21%.
Special Activity Airspaces (SAAs) are used in the National Airspace System (NAS) to segregate some users’ operations or activities from other users for safety reasons, such as military flight operations, missile tests, skydiving activity, and firefighting support for wildfires. New entrants into the NAS will require new airspace concepts that ensure safety but are more responsive than traditional airspace closure methods. The FAA’s Special Use Airspace Management System (SAMS) is intended to be the authoritative source for all SAA information and contains weekly and daily updates to the default SAA schedule that is distributed annually by the National Airspace System Resource (NASR) system. The real-time activation or deactivation of an SAA is managed by each Air Route Traffic Control Center (ARTCC). This is done collaboratively with using agency (e.g. military) operators within their airspace to determine the actual activation status of an SAA based on the using agency’s needs throughout the day. This real-time status is set in the Enroute Automation Modernization (ERAM) system and published out on System Wide Information Management (SWIM) via the SWIM Flight Data Publication Service (SFDPS), but it is not correlated with the SAA information in SAMS. This results in differences of SAA activation/deactivation status as well as definitions (i.e. geometric shape of the restricted airspace), naming, and schedule. Aeronautical Common Services (ACS), which is planned for deployment in late 2021, will provide consolidated SAA information from SAMS and NASR via SWMI, but not ERAM. Furthermore, neither system provides any type of SAA forecasting capability.
†Computational h uman performance models ar e useful for predicting operator workload under future concepts. The scan pattern behavior in these models can be improved by assuming that operator scan pattern is driven by valuable event information in expected locations at expected times . As applied to the air traffic control domain, this means scan pattern behavior can be driven by traffic density within user -defined are as of interest on the controller display. The areas of interest , manually created by dividing the controller display into non -overlapp ing regions, indicate the importance and value of the tasks performed in those areas of interest ( e.g., separation, spacing, merging, taking handoffs , and giving handoffs). Eye tracking data collected from the Future En Route Work Station (FEWS) experiment at the FAA Technical Center was utilized to calibrate and validate the model of scanning pattern behavior.
This research proposes and analyzes an approach for predicting controller workload by predicting dynamic density. Most dynamic density formulations estimate workload with a linear combination of a set of dynamic density factors that describe the trac situation in a sector. The robust approach proposed here uses this linear structure and the available data to explicitly consider the relative levels of uncertainty in dynamic density factor predictions when predicting dynamic density. The benets of the robust approach are analyzed by using predicted and actual dynamic density factor data collected while playing back trac data in the Future ATM Concepts Evaluation Tool. Results indicate that the robust approach produces errors that are more than an order of magnitude smaller than those produced by a simple approach that ignores factor prediction uncertainties. However, other approaches achieve lower prediction errors than the proposed robust approach.
Terminal operations that contain two or more closely-spaced airports and require significant interdependencies between the operations of those airports are referred to as metroplex operations. Examples of the interdependent techniques used by today’s metroplexes include: coordinated design of routings, synchronization of runway configurations across multiple airports, application of altitude restrictions to deconflict flows between airports, the creation of alternate procedures to avoid potential route interactions, and procedures for coordinating the timing of the release of flights from one or more airports. Research into future dynamic metroplex airspace (DMA) operations is investigating optimization methods to utilize metroplex airspace more efficiently in response to fluctuating arrival and departure demand to each of the metroplex airports. Quantifying the benefits of DMA is a necessary step in the research. The motivation behind this work is the need to provide an apple-to-apple comparison between today’s metroplex operations and future DMA operations. Trajectory clustering techniques using historical traffic data have been developed to determine the as-flown route structures in today’s metroplex airspace. These route structures provide insight into the deconfliction of flows through spatial separation (vertical and lateral). Additionally, the route structures have been utilized in a fast-time simulation to measure today’s flight efficiency metrics to enable comparison with future DMA operations.
A number of Federal Aviation Administration (FAA) automation systems and developmental programs interoperate to support the day-to-day operations of the National Airspace System (NAS). The integration of New Entrants (NEs), such as Remotely Piloted Aircraft (RPA) 1 and Space Vehicle (SV) operations, into the NAS will require modification to the current state of automation. Examining the relevant operational, technical, and procedural implications of integrated NE operations, in addition to exploring the potential impacts of accelerated RPA usage on NAS dynamics, are critically important efforts necessary to understanding operational impacts to NAS systems and users. Our paper presents the potential impacts of RPA integration on two of these systems: En Route Automation Modernization (ERAM) and Traffic Flow Management System (TFMS). A significant component of the impact on NAS systems comes from flight plan and related data processing since all SVs and many RPAs will fly non-standard flight patterns (e.g., RPA loitering and grid search). The processes and details of the non-traditional flight routes, as well different RPA performance specifications, need to be considered as missions are overlaid on the national jet route structure. Of particular concern and interest are the changes in air traffic control (ATC) and air traffic management (ATM) systems and sub-systems needed to address air traffic controller workload and workload complexity concerns associated with NE activities. One potential accommodation approach, presented in this paper, is the use of Dynamic Airspace Configuration both for RPA routine operations and contingency events. Additionally, we present an RPA 1 In this paper, UAS and RPA are used interchangeably without intending any difference per se. It is noted that the U.S. DoD and ICAO use RPA while NASA and the FAA use UAS. demand forecast tool developed under a National Aeronautics and Space Administration (NASA) project. This tool will assist the FAA in meeting its objectives for accommodation of RPA by providing accurate demand forecast information.
Management by Trajectory (MBT) is a NASA concept for taking Trajectory Based Operations (TBO) to the next level of maturity. MBT looks beyond enabling technologies to flesh out the operational concept and look at how TBO changes the ways in which actors interact. In MBT, each aircraft has an assigned trajectory that is negotiated between the Federal Aviation Administration (FAA) and airspace user (AU) and complies with all National Airspace System (NAS) constraints. Any deviation from the assigned trajectory must be negotiated. In addition to the request/response interactions between controllers and pilots that have so far been proposed to support Data Comm, trajectory negotiation under MBT leverages emerging capabilities to support much richer negotiations. MBT proposes a higher level language to support new kinds of requests and responses, such as providing new or amended constraints to which a trajectory must conform and allowing the trajectory to be proposed by the AU, or controllers offering solution options to the AU for the AU to select the preferred solution. The key MBT benefit mechanisms are trajectory predictability and AU flexibility. Improved predictability supports more efficient operations, from earlier and less frequent conflict detection and resolution to better calibration of traffic flow management decision-making to balance demand with capacity. Improved flexibility allows AUs to act on their trajectory preferences. This paper provides an overview of the MBT concept and discusses open questions to be explored in future concept evaluation activities.
