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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Expanding Regional Airport Usage to Accommodate Increased Air Traffic Demand

Small regional airports present an underutilized source of capacity in the national air transportation system. This study sought to determine whether a 50 percent increase in national operations could be achieved by limiting demand growth at large hub airports and instead growing traffic levels at the surrounding regional airports. This demand scenario for future air traffic in the United States was generated and used as input to a 24-hour simulation of the national airspace system. Results of the demand generation process and metrics predicting the simulation results are presented, in addition to the actual simulation results. The demand generation process showed that sufficient runway capacity exists at regional airports to offload a significant portion of traffic from hub airports. Predictive metrics forecast a large reduction of delays at most major airports when demand is shifted. The simulation results then show that offloading hub traffic can significantly reduce nationwide delays.

Russell, Carl R.↗
NASA NTRS2009

Convective Weather Avoidance with Uncertain Weather Forecasts

Convective weather events have a disruptive impact on air traffic both in terminal area and in en-route airspaces. In order to make sure that the national air transportation system is safe and efficient, it is essential to respond to convective weather events effectively. Traffic flow control initiatives in response to convective weather include ground delay, airborne delay, miles-in-trail restrictions as well as tactical and strategic rerouting. The rerouting initiatives can potentially increase traffic density and complexity in regions neighboring the convective weather activity. There is a need to perform rerouting in an intelligent and efficient way such that the disruptive effects of rerouting are minimized. An important area of research is to study the interaction of in-flight rerouting with traffic congestion or complexity and developing methods that quantitatively measure this interaction. Furthermore, it is necessary to find rerouting solutions that account for uncertainties in weather forecasts. These are important steps toward managing complexity during rerouting operations, and the paper is motivated by these research questions. An automated system is developed for rerouting air traffic in order to avoid convective weather regions during the 20- minute - 2-hour time horizon. Such a system is envisioned to work in concert with separation assurance (0 - 20-minute time horizon), and longer term air traffic management (2-hours and beyond) to provide a more comprehensive solution to complexity and safety management. In this study, weather is dynamic and uncertain; it is represented as regions of airspace that pilots are likely to avoid. Algorithms are implemented in an air traffic simulation environment to support the research study. The algorithms used are deterministic but periodically revise reroutes to account for weather forecast updates. In contrast to previous studies, in this study convective weather is represented as regions of airspace that pilots are likely to avoid. The automated system periodically updates forecasts and reassesses rerouting decisions in order to account for changing weather predictions. The main objectives are to reroute flights to avoid convective weather regions and determine the resulting complexity due to rerouting. The eventual goal is to control and reduce complexity while rerouting flights during the 20 minute - 2 hour planning period. A three-hour simulation is conducted using 4800 flights in the national airspace. The study compares several metrics against a baseline scenario using the same traffic and weather but with rerouting disabled. The results show that rerouting can have a negative impact on congestion in some sectors, as expected. The rerouting system provides accurate measurements of the resulting complexity in the congested sectors. Furthermore, although rerouting is performed only in the 20-minute - 2-hour range, it results in a 30% reduction in encounters with nowcast weather polygons (100% being the ideal for perfectly predictable and accurate weather). In the simulations, rerouting was performed for the 20-minute - 2-hour flight time horizon, and for the en-route segment of air traffic. The implementation uses CWAM, a set of polygons that represent probabilities of pilot deviation around weather. The algorithms were implemented in a software-based air traffic simulation system. Initial results of the system's performance and effectiveness were encouraging. Simulation results showed that when flights were rerouted in the 20-minute - 2-hour flight time horizon of air traffic, there were fewer weather encounters in the first 20 minutes than for flights that were not rerouted. Some preliminary results were also obtained that showed that rerouting will also increase complexity. More simulations will be conducted in order to report conclusive results on the effects of rerouting on complexity. Thus, the use of the 20-minute - 2-hour flight time horizon weather avoidance teniques performed in the simulation is expected to provide benefits for short-term weather avoidance.

Karahan, Sinan↗
NASA NTRS2003

TAMDAR Datalink Development

This viewgraph presentation provides information on the development of a downlink system for the TAMDAR (Tropospheric Airborne Meteorological Data Reporting) system of sensors mounted on individual airplanes. These sensors will be used by forecast models, weather briefers, air traffic controllers, and other aircraft. They will have the ability to monitor and report moisture, temperature, and wind characteristics below 20,000 feet altitude. The presentation discusses, with flowcharts, the various downlinking interconnections and network architectures.

Andro, Monty↗
NASA NTRS2014

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours.

Bloem, Michael↗
NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗
NASA NTRS2013

Objective Lightning Probability Forecasts for East-Central Florida Airports

The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

Crawford, Winfred C.↗
NASA NTRS2010

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

Russell, Carl↗
NASA NTRS2010

Interaction of Airspace Partitions and Traffic Flow Management Delay

To ensure that air traffic demand does not exceed airport and airspace capacities, traffic management restrictions, such as delaying aircraft on the ground, assigning them different routes and metering them in the airspace, are implemented. To reduce the delays resulting from these restrictions, revising the partitioning of airspace has been proposed to distribute capacity to yield a more efficient airspace configuration. The capacity of an airspace partition, commonly referred to as a sector, is limited by the number of flights that an air traffic controller can safely manage within the sector. Where viable, re-partitioning of the airspace distributes the flights over more efficient sectors and reduces individual sector demand. This increases the overall airspace efficiency, but requires additional resources in some sectors in terms of controllers and equipment, which is undesirable. This study examines the tradeoff of the number of sectors designed for a specified amount of traffic in a clear-weather day and the delays needed for accommodating the traffic demand. Results show that most of the delays are caused by airport arrival and departure capacity constraints. Some delays caused by airspace capacity constraints can be eliminated by re-partitioning the airspace. Analyses show that about 360 high-altitude sectors, which are approximately today s operational number of sectors of 373, are adequate for delays to be driven solely by airport capacity constraints for the current daily air traffic demand. For a marginal increase of 15 seconds of average delay, the number of sectors can be reduced to 283. In addition, simulations of traffic growths of 15% and 20% with forecasted airport capacities in the years 2018 and 2025 show that delays will continue to be governed by airport capacities. In clear-weather days, for small increases in traffic demand, increasing sector capacities will have almost no effect on delays.

Palopo, Kee↗
NASA NTRS2013

The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

trajectory prediction uncertainty↗
NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗
NASA NTRS1992

NASA Annual HSR Workshop: Boeing HSCT program summary

Boeing's view of the High Speed Civil Transport (HSCT) is given in viewgraph format. Boeing believes that the forecast for long range scheduled international traffic is sufficiently large in the post year 2005 time period to support a fleet of HSCT's. Technologies are projected to be available to create an HSCT that will have the required performance and operating economics, and which can be sold at a price that will provide a reasonable return to Boeing and the airlines. With relatively modest surcharges over competing subsonic fares, it is expected that an HSCT providing roughly a 50 percent time savings would capture a significant market share.

Henderson, Michael L.↗
NASA NTRS2010

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Sensor Validation and Verification on National Oceanographic and Atmospheric Administration (NOAA) Lockheed WP-3D Aircraft

As part of the National Aeronautics and Space Administration's Aviation Safety and Security Program, the Tropospheric Airborne Meteorological Data Reporting project (TAMDAR) developed a low-cost sensor for aircraft flying in the lower troposphere. This activity was a joint effort with support from Federal Aviation Administration, National Oceanic and Atmospheric Administration, and industry. This paper reports the TAMDAR sensor performance validation and verification, as flown on board NOAA Lockheed WP-3D aircraft. These flight tests were conducted to assess the performance of the TAMDAR sensor for measurements of temperature, relative humidity, and wind parameters. The ultimate goal was to develop a small low-cost sensor, collect useful meteorological data, downlink the data in near real time, and use the data to improve weather forecasts. The envisioned system will initially be used on regional and package carrier aircraft. The ultimate users of the data are National Centers for Environmental Prediction forecast modelers. Other users include air traffic controllers, flight service stations, and airline weather centers. NASA worked with an industry partner to develop the sensor. Prototype sensors were subjected to numerous tests in ground and flight facilities. As a result of these earlier tests, many design improvements were made to the sensor. The results of tests on a final version of the sensor are the subject of this report. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated air speed, true air speed, ice presence, wind speed and direction, and eddy dissipation rate. Summary results from the flight test are presented along with corroborative data from aircraft instruments.

Tsoucalas, George↗
NASA NTRS2018

Air Traffic Management Technology Demonstration - 3 (ATD-3) Dynamic Weather Routes Domestic En Route Concept of Operations Synopsis Version 1.0

NASA conducted a survey in 2015 of stakeholders in industry, flight operators, and others to collect data on the most pressing air traffic management (ATM) issues impacting en route operations. Weather related delays and interruptions were identified as the most significant and costly problem impacting en route operations in today's system. Additionally, future traffic demands will place greater demands on the FAA's air traffic control system, necessitate a more efficient and integrated traffic management system and provide controller tools to leverage human productivity. Consequently, NASA is assembling a suite of integrated ground and aircraft-based technologies and decision-making aids under the Airspace Technology Demonstrations 3 (ATD-3) project to continuously search for more efficient weather-avoidance routes and to rapidly respond to weather changes and associated traffic management initiatives. These route efficiency enhancements are targeted at en route airspace and will enable continued use of en route and arrival metering in the presence of weather. NASA's Dynamic Weather Routes (DWR) concept represents a subset of those technologies and focuses primarily on the cruise phase of flight. It leverages existing air traffic weather, airspace, and traffic data, as well as improvements in navigation, surveillance, communication and digital network technologies, and builds upon existing ATM automation to address the shortcomings associated with strategic traffic flow management initiatives and weather forecasting uncertainties. The potential benefits in the form of time, fuel, and cost savings are significant. This concept of operations synopsis provides a detailed description of DWR, its potential benefits and notional implementation paths.

DWR↗
NASA NTRS2018

Air Traffic Management Technology Demonstration - 3 (ATD-3) Multi-Flight Common Route (MFCR) Concept of Operations Version 1.0

NASA's Multi Flight Common Route (MFCR) automation represents one element of those technologies focusing primarily on delay recovery in the en route phase of flight. Delay recovery is an attenuation of flight-time delay, accomplished by periodically revising weather-avoidance routing as the convective weather system evolves. MFCR is intended for use by Traffic Management Coordinators (TMCs) in Air Route Traffic Control Centers (ARTCCs, or Centers) and traffic management specialists (TMSs) in the Air Traffic Control System Command Center (ATCSCC). MFCR leverages existing weather, airspace, and traffic data, as well as improvements in navigation, surveillance, communication, and digital information technologies, to build on existing ATM automation and address some of the shortcomings associated with strategic traffic flow management initiatives and weather forecasting uncertainties. These capabilities provide significant potential benefits in the form of time, fuel, and cost savings. The concept of operations described in this document describes MFCR functionality as delivered by NASA to the FAA in December 2017, including a list of potential enhancements that may be realized when the system is fielded.

conops↗
NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗
DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗
NASA NTRS2011

Traffic Flow Management Wrap-Up

Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.

Grabbe, Shon↗