A fully-dynamic network flow model of the NAS

In this paper we analyze alternative near-term Traffic Flow Management Operational Incentives (OPI). These OPIs are part of a larger effort to help accelerate airlines’ equipage efforts with respect to NextGen avionics in the areas of Communication, Navigation, and Surveillance. We analyze alternative operational incentives that can be implemented through the slot allocation process in a Ground Delay Program. Potential incentives can either provide natural benefits through capacity increase, or redistribute ground delays in a zero-sum fashion in favor of equipped flights. We argue that, due to the complex nature of airline operations, quantifying the benefits of these incentives can be challenging. Furthermore, as airlines compete for limited resources in the National Airspace System, incentives may trigger different actions among airlines. Thus we propose a marginal analysis with respect to varying equipage mixtures to study the local and system-wide ramifications of possible equipage decisions made by airlines in reaction to each incentive. This analysis framework can aid policymakers in selecting the appropriate operational incentive mechanisms to yield desirable outcomes. ull realization of NextGen benefits to the National Airspace System (NAS) relies upon aircraft being equipped with certain technological systems. However, the equipage process has faced impediments that have slowed equipage rates below targets. These impediments include high costs of acquisition, retrofitting, and crew training. Furthermore, uncertainty regarding return on investment in terms of the time until benefits start to accrue, as well as the actual magnitude of the benefits, complicates the decision-making process for the airlines to equip. Moreover, due to the highly competitive nature of airline operations, unresolved issues, such as early adopter versus free rider , exacerbate the uncertainty among airlines in making the decision to equip their fleets. It is believed that a combination of financial 1 and operational incentives 2,3 can help accelerate the equipage process by alleviating some uncertainty regarding the realization of the benefits associated with equipage. Previous studies 2,4 have indicated that strategic-level Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) and Airspace Flow Programs (AFP) can be used as a vehicle for Traffic Flow Management (TFM) Operational Incentives (OPI). Such incentives can be implemented by changing the resource allocation algorithms used to assign ground delays to flights. These alterations affect the distribution of benefits—in the form of delay reductions—among equipped and unequipped flights. The Joint Planning and Development Office (JPDO) offers an in-depth analysis of resource allocation and flight prioritization mechanisms in TMIs. 5 We classify TFM OPIs into two major categories based on how benefits are generated. The first category of incentives is associated with transitional benefits, in which equipage may not result in a near-term increase in capacity or throughput. The second category of incentives is associated with natural benefits, wherein increased capacity or throughput arises from the equipage and the consequent improvement in the aircraft performance.

A high-level state model of the national airspace system (NAS) has been developed and refined. That model provides guidance on the effect and interactions of sources of uncertainty in the NAS. The model also provides guidance on the possible effect of NextGen concepts and technologies on the NAS. The model is being coded as a high-level, abstract simulation that, when provided with specific uncertainty distributions, can also identify the emergent effect of those changes in the uncertainty distributions. The results of that analysis and simulation provide guidance for prioritizing the development and adoption of NextGen concepts and technologies for the NAS. I. Introduction he National Airspace System (NAS) is a highly complex, socio-technical system. At its lowest level, it consists of many thousands of entities, including aircraft and other sophisticated technology, passengers, pilots, and air traffic controllers. Modeling and simulation of such a system is difficult due to the large number of entities to be modeled, the infinite (or nearly so) number of degrees of freedom within the system, and the high degree of possible interactivity in the system. As a result, existing models/simulations must simplify the system by eliminating from consideration certain aspects, and therefore cannot be completely accurate in determining the effect of uncertainties on the performance of the NAS. To try to fill this gap in capability, a model of the NAS was developed, with the purpose of determining the effect of the various sources of uncertainty on the system performance of the NAS. This model is state-based, and models the system from a high level (Landry and Archer, 2013). The model is believed to be complete. Given this model, one can reason about the effects of uncertainty on the NAS, as well as the interactions of these sources of uncertainty (Landry and Archer, 2013). In addition, if one characterizes the effect of different operational concepts and technologies being considered under NextGen in terms of their effect on those sources of uncertainty, one can then reason about the relative benefits of those different concepts and technologies. In this work, we focus on the effect of concepts and technologies on the performance of the NAS. Following a brief discussion of the model, an enumeration of the technologies and concepts under consideration for NextGen will be presented, including their anticipated degree of effect on various sources of uncertainty. Lastly, the results of simulation exercises to test the model will be presented. II. Model of the NAS: Framework

An Eulerian network model for air traffic flow in the National Airspace System is developed and used to design flow control schemes which could be used by Air Traffic Controllers to optimize traffic flow. The model relies on a modified version of the Lighthill-Whitham-Richards (LWR) partial differential equation (PDE), which contains a velocity control term inside the divergence operator. This PDE can be related to aircraft count, which is a key metric in air traffic control. An analytical solution to the LWR PDE is constructed for a benchmark problem, to assess the gridsize required to compute a numerical solution at a prescribed accuracy. The Jameson-Schmidt-Turkel (JST) scheme is selected among other numerical schemes to perform simulations, and evidence of numerical convergence is assessed against this analytical solution. Linear numerical schemes are discarded because of their poor performance. The model is validated against actual air traffic data (ETMS data), by showing that the Eulerian description enables good aircraft count predictions, provided a good choice of numerical parameters is made. This model is then embedded as the key constraint in an optimization problem, that of maximizing the throughput at a destination airport while maintaining aircraft density below a legal threshold in a set of sectors of the airspace. The optimization problem is solved by constructing the adjoint problem of the linearized network control problem, which provides an explicit formula for the gradient. Constraints are enforced using a logarithmic barrier. Simulations of actual air traffic data and control scenarios involving several airports between Chicago and the U.S. East Coast demonstrate the feasibility of the method.

The current demand in the National Airspace System (NAS) is rapidly approaching the system capacity. This capacity limit causes significant congestion and delay in off-nominal conditions, such as inclement weather. It will also constrain future growth in the aviation sector. Transformation of the current system can relieve this constraint. Examples of such transformation include (i) flight schedule shifting to periods of low demand or alternate airports, (ii) air traffic control (ATC) productivity enhancements, (iii) new procedures and airspace redesigns, and (iv) improvements in weather nowcasting and forecasting. Planners are now investigating such NAS architecture strategies. This paper describes research and plans regarding our investigation of future NAS performance in inclement weather days. Our NAS simulation allows researchers and decision makers to hypothesize strategies in various weather conditions and traffic demand levels to quantify the combined impact on the NAS. The results of the simulation will guide decisions regarding the value of investments in weather forecasting technology and NAS procedures. Our simulation approach is to use (i) archived weather and traffic schedule data with (ii) weather and traffic forecasting models and (iii) tactical weather vectoring and stochastic traffic flow management capabilities.

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.

Airline-Driven Ground Delay Programs: A Benefits Assessment

The Ration by Schedule (RBS) algorithm has been accepted by the aviation community as the basis of Collaborative Decision Making (CDM) in strategic Traffic Flow Management (TFM) initiatives such as Ground Delay Programs (GDPs) and Airspace Flow Programs (AFPs). However, applications of RBS to date have been limited to strategic metering applications in which the slots to be allocated to flights are all of equal duration, and the flights to be assigned to slots do not need to be differentiated. Decision support capabilities for the general Air Traffic Management (ATM) problem require planning and scheduling algorithms that properly recognize different flight characteristics such as weight class, runway, departure direction and other Air Traffic Control (ATC) procedural issues in determining the slot size that is required for each flight. In this paper, we describe our design and implementation of the Generalized RBS algorithm for a surface traffic management scheduling application which involves detailed flight-by-flight slot time assignment. This algorithm has been designed and developed to support the Collaborative Departure Queue Management (CDQM) component of the Surface Trajectory Based Operations (STBO) project. CDQM provides an equitable allocation of departure capacity to each flight operator at an airport for dynamic use in management of departure queues. The principles of the RBS algorithm are achieved through an initial planning pass based on scheduled data, followed by a second planning pass in which dynamic flight status is incorporated into the planning results.

This paper discusses the results of modeling and simulation performed by FAA’s Performance Analysis and Strategy office to forecast delays at thirty major US airports six months ahead of time. In order to have reliable delay forecasts, it is important that the simulation model validates well with historic actual data. As part of the validation efforts, we recognize that an aircraft’s movement in the US National Airspace System (NAS) involves its interaction with various resources along the flight path, such as airport runways, arrival and departure fixes, airspace sectors, and terminal gates. When multiple aircraft compete for access to these resources, their movements are impeded due to operational and safety considerations, resulting in flight delays. The objective of this study is to understand how various resources contribute to total flight delays, and how sensitive the delays are to changes in resource capacities. The results to date indicate that reduced airport runway capacities play a significant role in overall delays, but arrival and departure fixes and airspace sector constraints do not. Our use of historical airport AARs and ADRs for runway capacities, which may inherently account for fix constraints, yields delays that agree very well with actual values reported in ASPM. However, care has to be taken to use and interpret these rates when there are runway additions or closures. One area in which all NAS-wide models fail to replicate the behavior of the NAS is gate-out delay. Our simulation includes modeled distributions to capture gate-out delays, but they were found to capture only a fraction of actual gate-out delays. Terminal gate constraints were added to verify if they yielded large gate-in delays that would result in additional gate-out delays. No significant additional gate-out delays were obtained, although we gained better insight into how gate-in delays may occur. We are continuing to research potential NAS interactions that would account for the gap in predicting gate-out delays. Additional simulation model enhancements are planned to account for traffic flow initiatives such as GDP, as well as itinerary adjustments due to flight cancellation and substitutions.

The U.S. National Airspace System (NAS), its interconnected operations, and resultant dynamics (e.g., flight delays, route configurations) can be understood at different scales. At the resolution of the entire NAS, a component (e.g., Denver Center) could be considered as one homogeneous node, with dependencies on other components (e.g., adjacent centers) and exogenous stakeholders (e.g., Air Traffic Control System Command Center, airline network operations centers). Understanding the connected behavior of the NAS in a data-driven and adaptive way is critical for rigorously determining whether interventions, strategic or tactical, were successful. However, within NAS components flight operations induce a multitude of relationships between parts of the airspace at many levels. To capture, analyze, and build upon such a connected and multi-scaled system, we require graph-based network models at varying resolutions, which can be adapted to fit a particular analysis use case. As an example, graphical representation at a higher resolution within a component may be required to capture nuanced behavior in analyses focused on local perturbations. In this work, we present a pipeline that constructs flexible graph-structured data from flight trajectories, and leverage this for different case studies within the NAS, all focused on evaluating different aspects of traffic flow management, and at different scales.

The charter of FAA is to promote the safe, orderly, and expeditious use of the National Airspace System (NAS). To ensure that traffic flow is safe and efficient, FAA needs to know the expected traffic demand on the sector and the sector's capacity to accommodate that demand. When sector capacity is inadequate to meet the demand, congestion occurs. To ensure that safety is not compromised, FAA often takes action to reduce demand or increase capacity to avoid congestion. The MITRE Corporation's Center for Advanced Aviation System Development developed a time-on-task workload model to assess capacity and congestion in en route NAS sectors. A metric was developed and used along with the workload generated by the model and a workload threshold to estimate sector capacity. The metric, as constructed, enabled equitable comparison of capacity of different sectors, regardless of size. A field and lab evaluation of the workload model was used to quantify the model's task coverage and to calibrate its parameter values. The workload model was used to generate workload, capacity, and congestion profiles for selected en route sectors during good weather and during convective weather. The data used to generate the profiles can be used for various air traffic management applications.

DOI: 10.2514/1.51203 This paper proposes a general modeling framework adapted to the feedback control of traffic flows in Eulerian modelsof the NationalAirspace System. It is shown thatthe problems of scheduling androuting aircraft flowsin the NationalAirspaceSystemcanbeposedasthecontrolofanetworkofqueueswithload-dependentservicerates.Focus can then shift to developing techniques to ensure that the aircraft queues in each airspace sector, which are an indicator of the air traffic controller workloads, are kept small. This paper uses the proposed framework to develop control laws that help prepare the National Airspace System for fast recovery from a weather event, given a probabilistic forecast of capacities. In particular, the model includes the management of airport arrivals and departures subject to runway capacity constraints, which are highly sensitive to weather disruptions. I. Introduction T HE frequent occurrence of air traffic delays in the National Airspace System (NAS), along with the projected increase in demand, motivate the scheduling of flight operations to better use available system resources. The process of planning operations to balancetheavailablecapacityandthedemandforresourcesisknown asTrafficFlowManagement(TFM).Thistaskiscurrentlyconducted manually by air traffic controllers (ATC), and contributes significantlytotheirworkload.Tomeettheincreasingtrafficdemand,there is a desire to introduce a greater level of automation and decision support for air traffic management. Research on the TFM problem has traditionally focused on developing open-loop policies for scheduling aircraft operations. Because of the typical travel times of cross-country flights, openloop traffic flow management policies need to be determined 5– 6 hours ahead of the time of operations. Such policies prescribe the positionofeachaircraftinthesystemateachinstant,andareobtained by solving large-scale integer programs [1,2]. This approach is difficult to scale to the scheduling of approximately 40,000 flights a day, and typically does not address the many sources of uncertainty present in the system. Weather, in particular, is a major source of disruption that requires constant adjustment of the schedules. For instance, 66% of all NAS delays in 2009 were attributed toweather. ‡ Moreover, open-loop traffic flow management algorithms require precise weather forecasts several hours ahead of time, which are arguably beyond the limits of even state-of-the-art weather forecasting tools [3]. The disturbance attenuation properties of feedback control make closed-loop control policies for the NAS very attractive. Attempts have been made to introduce some feedback in the decision algorithmswhilestilltryingtooptimizeeachaircrafttrajectory[4,5]. More recently, researchers have started developing new models that are more tractable for the purpose of control, which only record aircraft counts in specific control volumes of airspace rather than follow individual aircraft. These aggregate flow models, called

The Airspace Concepts Evaluation System (ACES) is a distributed fast-time computer simulation used for design and trade-off studies of system level concepts for the National Airspace System (NAS). ACES employs multi-trajectory modeling in an agent-based software structure to describe the behavior and interactions of Traffic Flow Management, Air Traffic Control, Flight Deck, and Airline Operational Control functions and associated technologies. ACES modules include airport, terminal, en route and system-wide traffic management and controller agents, dispatcher agents, and flight agents; individual aircraft flight dynamics; and airport, airspace, meteorological, and related infrastructure components. ACES simulates gate-to-gate flight operations through the multi-sector, multiCenter, multi-airport NAS network. This paper focuses on the design and implementation of enhanced terminal area modeling capabilities in ACES Build 3. Previous builds of ACES have implemented a nodal model to describe runway system utilization at each airport with nominal terminal airspace transit times. The Build 3 capabilities provide improved modeling fidelity by accounting for individual runway operations and enabling the specification of additional detail in terminal airspace network operations. The Runway Modeling capability simulates individual aircraft operations on individual runways based on user-specified runway use configurations and runway interactions at an airport. The Simplified Terminal Airspace Network Modeling capability simulates irregular terminal airspace boundaries, variable terminal transit times and multiple airport operations in a terminal area. ACES Build 3 introduces capabilities to apply more complex terminal area modeling to selected airports while retaining the lesscomplex nodal model at other airports. ACES also supports variable complexity modeling by enabling users to define the time span over which Traffic Flow Management agents have accurate knowledge of upcoming airport operating conditions (versus inaccurate knowledge based on extrapolated forecasts) and to define the mode by which traffic flow restrictions are simulated (i.e., determined by runway system operating dynamics versus manually-defined static acceptance rates). These features support the modeling of decision support tools of variable levels of sophistication, enabling ACES users to assess the relative value of alternative advanced tool development efforts.

** † ‡ Development of a decision support system that uses real-time track data to estimate statistical parameters describing the stochastic traffic flow is described. Modern statistical decision theory is applied to optimize traffic flow. An advanced estimation algorithm provides the parameter estimates based on queuing network models of traffic flow. A hypothesis testing approach is developed for triggering traffic flow management initiatives in the terminal area, and a stochastic quadratic programming methodology is advanced to achieve flow control objectives such as runway load balancing. The use of this methodology is demonstrated using multi-day track data in the San Francisco terminal area. It is shown that the methodology can correctly identify the need for restricting the traffic flow into the terminal area, and provide decision support to balance the traffic flow at the runways under uncertain traffic flow conditions. The present approach can be extended to the creation of decision support tools for a wide variety of stochastic air traffic flow control situations.

This paper presents a methodology for the mathematical characterization of three-dimensional (3-D) airspace traffic flows from flight position data. The methodology may be applied to flight data in the en route and terminal-area environments for such airspace applications as the creation of a traffic flow network model, route design based upon overlays of current traffic patterns, and benefits assessment. The methodology is applied to arrivals to the Philadelphia International (PHL) airport and to departures from the Dulles International (IAD) airport. The methodology selects a flight that best characterizes the 3-D distribution of flight position data for flights with a common origin and destination. This characteristic flight, called the typical flight, is most centrally located in an airspace volume about a traffic flow. The typical track respects traffic flow patterns present in the data. The typical track algorithm also provides a means of classifying different traffic flow patterns within an overall traffic flow which can be related to assessing airspace efficiency. This paper presents a method for converting the selected typical track to a route within the context of an area navigation (RNAV) procedure design. An example of using the typical track to categorize flight patterns for the PHL arrival data set shows how this may guide RNAV benefits assessment in a Terminal Radar Approach Control (TRACON).

Electric propulsion and autonomy are technology frontiers that offer tremendous potential to achieve low operating costs for small-aircraft. Such technologies enable simple and safe to operate vehicles that could dramatically improve regional transportation accessibility and speed through point-to-point operations. This analysis develops an understanding of the potential traffic volume and National Airspace System (NAS) capacity for small on-demand aircraft operations. Future demand projections use the Transportation Systems Analysis Model (TSAM), a tool suite developed by NASA and the Transportation Laboratory of Virginia Polytechnic Institute. Demand projections from TSAM contain the mode of travel, number of trips and geographic distribution of trips. For this study, the mode of travel can be commercial aircraft, automobile and on-demand aircraft. NASA's Airspace Concept Evaluation System (ACES) is used to assess NAS impact. This simulation takes a schedule that includes all flights: commercial passenger and cargo; conventional General Aviation and on-demand small aircraft, and operates them in the simulated NAS. The results of this analysis projects very large trip numbers for an on-demand air transportation system competitive with automobiles in cost per passenger mile. The significance is this type of air transportation can enhance mobility for communities that currently lack access to commercial air transportation. Another significant finding is that the large numbers of operations can have an impact on the current NAS infrastructure used by commercial airlines and cargo operators, even if on-demand traffic does not use the 28 airports in the Continental U.S. designated as large hubs by the FAA. Some smaller airports will experience greater demand than their current capacity allows and will require upgrading. In addition, in future years as demand grows and vehicle performance improves other non-conventional facilities such as short runways incorporated into shopping mall or transportation hub parking areas could provide additional capacity and convenience.