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NASA NTRS2015

Assessment of a National Airspace System Airborne Rerouting Tool

This paper presents the assessment of a National Airspace System airborne rerouting tool. The tool implements NASA’s Dynamic Weather Routes concept for wind-corrected flying-time savings during convective weather activity. A description of the system, as applicable to the entire United States airspace is provided, and results are presented demonstrating benefits of such a system from various Centers and airlines’ perspectives. Three cases for selection of reroute-return capture fix, which prevent unrealistically large controller clearances are presented. Results are shown for potential time- and fuel-savings (over 134,000 minutes and 4.2 million lbs. of fuel for over 35,000 proposed reroutes) and sector congestion reduction (over 121 hours in congested sectors) for all 20 Centers. The data used were for 30 days with highest delays attributable to convective weather from April to October of 2014. Other results show the evaluation of the maneuver or reroute start point (a parameter representing the amount of coordination time needed), which highlight the need for a controller-pilot data link. A data link would help achieve higher savings. The results for persistence time, beyond which the time-savings dwindle quickly, help determine the maximum coordination time required for each Center. Finally, an assessment from a current National Operations Manager at the Air Traffic Control System Command Center of the FAA is documented. Those suggestions could improve the efficiency of the air transportation system, especially with the expected improvements in the traffic flow management infrastructure. Currently, one industry partner and one airline are assessing this technology for commercial operational use.

Benefits Assessment↗
NASA NTRS2017

Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories

Traffic flow management seeks to balance the demand for National Airspace System (NAS) flight resources, such as airspace and airports, with the available supply. When forecasted weather blocks nominal air traffic routes, traffic managers must re-route affected flights for weather avoidance. Depending on the nature and scope of the weather, traffic managers may use pre-coordinated re-routes such as Playbook Routes or Coded Departure Routes, or may design ad hoc local re-routes. The routes of affected flights are modified accordingly. These weather avoidance routes will, of course, be less efficient than the nominal routes due to increased flight time and fuel burn. In current traffic management operations, the transition into a weather avoidance re-routing initiative is typically implemented more aggressively than the transition out of that initiative after the weather has dissipated or moved away. For example, strategic large-scale Playbook re-routes are sometimes left in place (as initially implemented) for many hours before being lifted entirely when the weather dissipates. There is an opportunity to periodically modify the re-routing plan as weather evolves, thereby attenuating its adverse impact on flight time and fuel consumption; this is called delay recovery. Multi-Flight Common Routes (MFCR) is a NASA-developed operational concept and associated decision support tool for delay recovery, designed to assist traffic managers to efficiently update weather avoidance traffic routes after the original re-routes have become stale due to subsequent evolution of the convective weather system. MFCR groups multiple flights to reduce the number of advisories that the traffic manager needs to evaluate, and also merges these flights on a common route segment to provide an orderly flow of re-routed traffic. The advisory is presented to the appropriate traffic manager who evaluates it and has the option to modify it using MFCRs graphical user interface. If the traffic manager finds the advisory to be operationally appropriate, he or she would coordinate with the Area Supervisor(s) of the sectors that currently control the flights in the advisory. When the traffic manager accepts the MFCR advisory via the user interface, the corresponding flight plan amendments would be sent to the displays of the appropriate sector controllers, using the Airborne Re-Routing (ABRR) capability which is scheduled for nationwide operation in 2017. The sector controllers would then offer this time-saving route modification to the pilots of the affected flights via datalink (or voice), and implement the corresponding flight plan amendment if the pilots accept it. MFCR is implemented as an application in the software environment of the Future Air traffic management Concepts Evaluation Tool (FACET). This paper focuses on an initial subject matter expert (SME) evaluation of MFCR. The evaluation covers MFCRs operational concept, algorithm, and user interface.

Human-in-the-loop Evaluation↗
NASA NTRS1986

A time-based concept for terminal-area traffic management

An automated air-traffic-management concept that has the potential for significantly increasing the efficiency of traffic flows in high-density terminal areas is discussed. The concept's implementation depends on techniques for controlling the landing time of all aircraft entering the terminal area, both those that are equipped with on-board four-dimensional (4D) guidance systems as well as those aircraft types that are conventionally equipped. The two major ground-based elements of the system are a scheduler which assigns conflict-free landing times and a profile descent advisor. Landing time provided by the scheduler is uplinked to equipped aircraft and translated into the appropriate 4D trajectory by the-board flight-management system. The controller issues descent advisories to unequipped aircraft to help them achieve the assigned landing times. Air traffic control simulations have established that the concept provides an efficient method for controlling various mixes of 4D-equipped and unequipped, as well as low- and high-performance, aircraft. Piloted simulations of profiles flown with the aid of advisories have verified the ability to meet specified descent times with prescribed accuracy.

Erzberger, Heinz↗
NASA NTRS1986

A time-based concept for terminal-area traffic management

An automated air-traffic-management concept that has the potential for significantly increasing the efficiency of traffic flows in high-density terminal areas is discussed. The concept's implementation depends on the techniques for controlling the landing time of all aircraft entering the terminal area, both those that are equipped with on-board four dimensional guidance systems as well as those aircraft types that are conventionally equipped. The two major ground-based elements of the system are a scheduler which assigns conflict-free landing times and a profile descent advisor. Landing times provided by the scheduler are uplinked to equipped aircraft and translated into the appropriate four dimensional trajectory by the on-board flight-management system. The controller issues descent advisories to unequipped aircraft to help them achieve the assigned landing times. Air traffic control simulations have established that the concept provides an efficient method for controlling various mixes of four dimensional-equipped and unequipped, as well as low-and high-performance, aircraft.

Erzberger, H.↗
NASA NTRS2014

A Study of Future Communications Concepts and Technologies for the National Airspace System-Part III

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is investigating current and anticipated wireless communications concepts and technologies that the National Airspace System (NAS) may need in the next 50 years. NASA has awarded three NASA Research Announcements (NAR) studies with the objective to determine the most promising candidate technologies for air-to-air and air-to-ground data exchange and analyze their suitability in a post-NextGen NAS environment. This paper will present progress made in the studies and describe the communications challenges and opportunities that have been identified as part of the study. NASA's NextGen Concepts and Technology Development (CTD) Project integrates solutions for a safe, efficient and high-capacity airspace system through joint research efforts and partnerships with other government agencies. The CTD Project is one of two within NASA's Airspace Systems Program and is managed by the NASA Ames Research Center. Research within the CTD Project is in support the 2011 NASA Strategic Plan Sub-Goal 4.1: Develop innovative solutions and advanced technologies, through a balanced research portfolio, to improve current and future air transportation. The focus of CTD is on developing capabilities in traffic flow management, dynamic airspace configuration, separation assurance, super density operations and airport surface operations. Important to its research is the development of human/automation information requirements and decisionmaking guidelines for human-human and human-machine airportal decision-making. Airborne separation, oceanic intrail climb/descent and interval management applications depend on location and intent information of surrounding aircraft. ADS-B has been proposed to provide the information exchange, but other candidates such as satellite-based receivers, broadband or airborne internet, and cellular communications are possible candidate's.

Navigation↗
NASA NTRS2015

Arrival Metering Precision Study

This paper describes the background, method and results of the Arrival Metering Precision Study (AMPS) conducted in the Airspace Operations Laboratory at NASA Ames Research Center in May 2014. The simulation study measured delivery accuracy, flight efficiency, controller workload, and acceptability of time-based metering operations to a meter fix at the terminal area boundary for different resolution levels of metering delay times displayed to the air traffic controllers and different levels of airspeed information made available to the Time-Based Flow Management (TBFM) system computing the delay. The results show that the resolution of the delay countdown timer (DCT) on the controllers display has a significant impact on the delivery accuracy at the meter fix. Using the 10 seconds rounded and 1 minute rounded DCT resolutions resulted in more accurate delivery than 1 minute truncated and were preferred by the controllers. Using the speeds the controllers entered into the fourth line of the data tag to update the delay computation in TBFM in high and low altitude sectors increased air traffic control efficiency and reduced fuel burn for arriving aircraft during time based metering.

human/systems↗
NASA NTRS2016

Benefit Opportunities for Integrated Surface and Airspace Departure Scheduling: A Study of Operations at Charlotte-Douglas International Airport

NASA is collaborating with the FAA and aviation industry to develop and demonstrate new capabilities that integrate arrival, departure, and surface air-traffic operations. The concept relies on trajectory-based departure scheduling and collaborative decision making to reduce delays and uncertainties in taxi and climb operations. The paper describes the concept and benefit mechanisms aimed at improving flight efficiency and predictability while maintaining or improving operational throughput. The potential impact of the technology is studied and discussed through a quantitative analysis of relevant shortfalls at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport. Results from trajectory analysis indicate substantial opportunity to reduce taxi delays for both departures and arrivals by metering departures at the gate in a manner that maximizes throughput while adhering to takeoff restrictions due mostly to airspace constraints. Substantial taxi-out delay reduction is shown for flights subject to departure restrictions stemming from traffic flow management initiatives. Opportunities to improve the predictability of taxi, takeoff, and climb operations are examined and their potential impact on airline scheduling decisions and air-traffic forecasting is discussed. In addition, the potential to improve throughput with departure scheduling that maximizes use of available runway and airspace capacity is analyzed.

air traffic management↗
NASA NTRS2017

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗
NASA NTRS2017

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗
NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗
NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗
NASA NTRS2015

Simulation and Analysis of Technology and Operational Procedures to Reduce the Combined Effects of Emissions and Contrails

The development and evaluation of concepts and technology to support future air traffic management systems require a hierarchy of models ranging from real-time simulations to extensive field evaluations. Air traffic simulation models such as Airspace Concept Evaluation System, Center Tracon Automation System, Future Air traffic management Concept Evaluation Tool and others are used to design air traffic systems balancing the conflicting objectives of maximizing safety, meeting future demands for airports and airspace and increase efficiency of traffic flows in the presence of uncertain weather. The impact of aviation emissions and contrails on climate imposes another constraint on the design of aircraft and aviation operations. The understanding of the complex interaction between physical climate system, the carbon and other greenhouse gas emissions and aviation activity can be improved by the development of integrated assessment models that include emission and climate models together with air traffic simulations. The modeling of aircraft emissions and their interaction with each other to change the concentration levels of different gasses in the atmosphere and the resulting impact of the radiative forcing on the equilibrium of the Earth's atmosphere is complex and requires the use of coupled atmosphere-ocean general circulation models together with three-dimensional models of carbon cycle and chemistry of other non-CO2 greenhouse gases. These models are computationally intensive and unsuitable for studies involving the generation of multiple scenarios. Simple emission and climate models, based on the input-output relations of linear systems, capture the fundamental emission to climate impact behavior by careful selection of key variables and their dynamics. The impact of various greenhouse gases depends on the total concentration, effect per unit change in atmospheric concentration and the spatial distribution of the gas. All these quantities are influenced by the lifetime of the gas. The impact of a greenhouse gas depends on the interval of assessment, which may vary from a few decades to a few centuries. Climate metrics are aimed at providing a common scale to compare different greenhouse gases. If the metrics are to be used as a tool in developing and evaluating aviation operations, they should be transparent and easy to apply. Global Warming Potential and Aggregate Global Temperature Potential are some of the commonly used metrics. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability brings together metrics, which are useful in aviation operations together with metrics used in climate studies. The capability can be used to make trade-offs between extra fuel cost and reduction in climate impact. There is considerable uncertainty in our understanding of the radiative forcing associated with emissions and contrails. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails. It can also be used to evaluate the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA's Aviation Environmental Portfolio Management Tool for Impacts.

impact of aviation on the environment↗
NASA NTRS2004

System for Better Spacing of Airplanes En Route

An improved method of computing the spacing of airplanes en route, and software to implement the method, have been invented. The purpose of the invention is to help air-traffic controllers minimize those deviations of the airplanes from the trajectories preferred by their pilots that are needed to make the airplanes comply with miles-in-trail spacing requirements. The software is meant to be a modular component of the Center TRACON Automation System (CTAS) (TRACON signifies "terminal radar approach control"). The invention reduces controllers workloads and reduces fuel consumption by reducing the number of corrective clearances needed to achieve conformance with specified flow rates, without causing conflicts, while providing for more efficient distribution of spacing workload upstream and across air-traffic-control sectors.

Green, Steven↗
NASA NTRS2000

A Conceptual Design of a Departure Planner Decision Aid

Terminal area Air Traffic Management handles both arriving and departing traffic. To date, research work on terminal area operations has focused primarily on the arrival flow and typically departures are taken into account only in an approximate manner. However, arrivals and departures are highly coupled processes especially in the terminal airspace, with complex interactions and sharing of the same airport resources between arrivals and departures taking place in practically every important terminal area. Therefore, the addition of automation aids for departures, possibly in co-operation with existing arrival flow automation systems, could have a profound contribution in enhancing the overall efficiency of airport operations. This paper presents the conceptual system architecture for such an automation aid, the Departure Planner (DP). This architecture can be used as a core in the development of decision-aiding systems to assist air traffic controllers in improving the performance of departure operations and optimize runway time allocation among different operations at major congested airports. The design of such systems is expected to increase the overall efficiency of terminal area operations and yield benefits for all stakeholders involved in Air Traffic Management (ATM) operations, users as well as service providers.

Anagnostakis, Ioannis↗
NASA NTRS1996

High-Speed Civil Transport Forecast: Simulated Airlines Scenarios for Mach 1.6, Mach 2.0, and Mach 2.4 Configurations for Year 2015

The report describes the development of a database of fuel burn and emissions from projected High Speed Civil Transport (HSCT) fleets that reflect actual airlines' networks, operational requirement, and traffic flow as operated by simulated world wide airlines for Mach 1.6, 2.0, and 2.4 HSCT configurations. For the year 2015, McDonnell Douglas Corporation created two supersonic commercial air traffic networks consisting of origin-destination city pair routes and associated traffic levels. The first scenario represented a manufacturing upper limit producible HSCT fleet availability by year 2015. The fleet projection of the Mach 2.4 configuration for this scenario was 1059 units with a traffic capture of 70 percent. The second scenario focused on the number of units that can minimally be produced by the year 2015. Using realistic production rates, the HSCT fleet projection amounts to 565 units. The traffic capture associated with this fleet was estimated at 40 percent. The airlines network was extracted from the actual networks of 21 major world airlines. All the routes were screened for suitability for HSCT operations. The route selection criteria included great circle distance, difference between flight path distance and great circle distance to avoid overland operations, and potential flight frequency.

Metwally, Munir↗
NASA NTRSDate not supplied

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗