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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Planning for New Primary Airports in the United States: A Survey of Metropolitan Planning Organizations

Airport congestion at primary airports in major metropolitan areas was analyzed in a report prepared by the Transportation Research Board (TRB) in 1990. Taking the top twenty-three most congested airports from this study, a questionnaire was prepared and sent to the metropolitan planning organizations (MPOS) for twenty of the twenty-three metropolitan areas represented in the TRB study. The questionnaire focused on the role of the MPOs in planning for new primary airports in the United States, including questions about the status of the most recent MPO airport system plan, whether or not the latest plan recommends a new primary airport, and whether or not any other entities in the MPO areas are recommending new primary airports. The results indicated that 44.4 percent of the eighteen respondent MPOs have airport system plans that are five years old or older. Also, only two of the respondent MPOs have recommended a new primary airport in their latest regional airport system plan and only one of these two is a common recommendation in the Federal Aviation Administration's National Plan of Integrated Airport System.

NewMeyer, David A.↗
NASA NTRS1999

Planning for New Primary Airports in the United States: A Survey of Metropolitan Planning Organizations

Airport congestion at primary airports in major metropolitan areas was analyzed in a report prepared by the Transportation Research Board (TRB) in 1990. Taking the top twenty-three most congested airports from this study, a questionnaire was prepared and sent to the metropolitan planning organizations (MPOs) for twenty of the twenty-three metropolitan areas represented in the TRB study, The questionnaire focused on the role of the MPOs in planning for new primary airports in the United States, including questions about the status of the most recent MPO airport system plan, whether or not the latest plan recommends a new primary airport, and whether or not any other entities in the MPO areas are recommending new primary airports. The results indicated that 44.4 percent of the eighteen respondent MPOs have airport system plans that are five years old or older. Also, only two of the respondent MPOs have recommended a new primary airport in their latest regional airport system plan and only one of these two is a common recommendation in the Federal Aviation Administration's National Plan of Integrated Airport System.

NewMyer, David A.↗
NASA NTRS2011

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Weather impact↗
NASA NTRS1971

Economic utilization of general aviation airport runways

The urban general aviation airport economics is studied in detail. The demand for airport services is discussed, and the different types of users are identified. The direct cost characteristics of the airport are summarized; costs to the airport owner are largely fixed, and, except at certain large airports, weight is not a significant factor in airport costs. The efficient use of an existing airport facility is explored, with the focus on the social cost of runway congestion as traffic density at the airport build up and queues form. The tradeoff between aircraft operating costs and airport costs is analyzed in terms of runway length. The transition from theory to practice is treated, and the policy of charging prices only on aircraft storage and fuel is felt likely to continue. Implications of the study from the standpoint of public policy include pricing that spreads traffic peaks to improve runway utilization, and pricing that discriminates against aircraft requiring long runways and causes owners to adopt V/STOL equipment.

Piper, R. R.↗
NASA NTRS2010

Integrated Analysis of Airport Capacity and Environmental Constraints

LMI conducted an integrated analysis of airport capacity and environmental constraints. identifying and ranking the key factors limiting achievement of NextGen capacity goals. The primary metric used was projected throughput, which was estimated for the years 2015 and 2025 based on the unconstrained demand forecast from the Federal Aviation Administration, and planned improvements including those proposed in the NextGen plan. A set of 310 critical airports was identified.. collectively accounting for more than 99 percent of domestic air traffic volume; a one-off analytical approach was used to isolate the constraint being assessed. The study considered three capacity constraints (runway.. taxiway, and gate) and three environmental constraints (fuel, NO(x) emissions, and noise). For the ten busiest airports, runway and noise are the primary and secondary constraints in both 2015 and 2025. For the OEP 35 airports and overall for the remaining airports, the most binding constraint is noise. Six of the 10 busiest airports, will face runway constraints in 2025, and 95 will face gate constraints. Nearly every airport will be subject to constraints due to emissions and NOx. Runway and taxi constraints are more concentrated in the large airports: environmental constraints are present at almost every airport regardless of size.

Hasan, Shahab↗
NASA NTRS2009

Progress on the Development of Future Airport Surface Wireless Communications Network

Continuing advances in airport surface management and improvements in airport surface safety are required to enable future growth in air traffic throughout the airspace, as airport arrival and departure delays create a major system bottleneck. These airport management and safety advances will be built upon improved communications, navigation, surveillance, and weather sensing, creating an information environment supporting system automation. The efficient movement of the digital data generated from these systems requires an underlying communications network infrastructure to connect data sources with the intended users with the required quality of service. Current airport surface communications consists primarily of buried copper or fiber cable. Safety related communications with mobile airport surface assets occurs over 25 kHz VHF voice and data channels. The available VHF spectrum, already congested in many areas, will be insufficient to support future data traffic requirements. Therefore, a broadband wireless airport surface communications network is considered a requirement for the future airport component of the air transportation system. Progress has been made on defining the technology and frequency spectrum for the airport surface wireless communications network. The development of a test and demonstration facility and the definition of required testing and standards development are now underway. This paper will review the progress and planned future work.

Kerczewski, Robert J.↗
NASA NTRS2017

Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Machine Learning Model.↗
DOE OSTI2025

A model for selecting the best sustainable airport technology alternatives

Air transport is a continually expanding industry, a fact that has become even more evident with the recovery of the aviation industry post-COVID-19. This expansion amplified energy consumption at airports, which was already significantly high. Airport decision-makers are increasingly focusing on improving sustainability and addressing social, economic, and environmental criteria across airports worldwide. In this article, we propose a decision-making model for identifying a sustainable airport technology solution that minimizes the energy consumption of airport lighting while considering economic, emission, and life-cycle criteria. The proposed model combines data envelopment analysis and multi-criteria decision making techniques. We applied the model to the Dallas/Fort Worth International Airport (DFW) as a case study, considering eight lighting technology solutions, each with five luminous flux alternatives. Our model identified the best lighting technology solution for DFW outdoor and indoor environments based on the following criteria: luminous flux, capital costs, life-cycle costs, energy consumption, and emissions (CO2e, NOx, SO2, and PM2.5). The designed model is customizable to any airport and is applicable to a wide range of airport lighting technologies. In our analysis, Light-Emitting Diode lighting emerged as the most sustainable technology option. It ranked first in most cases due to its balance of high efficacy, long lifespan, low life-cycle cost, low capital cost, and lower emissions across all pollutants.

Tchivwila, Moise B↗
NASA NTRS1998

The Aviation System Analysis Capability Airport Capacity and Delay Models

The ASAC Airport Capacity Model and the ASAC Airport Delay Model support analyses of technologies addressing airport capacity. NASA's Aviation System Analysis Capability (ASAC) Airport Capacity Model estimates the capacity of an airport as a function of weather, Federal Aviation Administration (FAA) procedures, traffic characteristics, and the level of technology available. Airport capacity is presented as a Pareto frontier of arrivals per hour versus departures per hour. The ASAC Airport Delay Model allows the user to estimate the minutes of arrival delay for an airport, given its (weather dependent) capacity. Historical weather observations and demand patterns are provided by ASAC as inputs to the delay model. The ASAC economic models can translate a reduction in delay minutes into benefit dollars.

Lee, David A.↗
NASA NTRS2003

An Analysis of Delay and Travel Times at Sao Paulo International Airport (AISP/GRU): Planning Based on Simulation Model

The occurrence of flight delays in Brazil, mostly verified at the ground (airfield), is responsible for serious disruptions at the airport level but also for the unchaining of problems in all the airport system, affecting also the airspace. The present study develops an analysis of delay and travel times at Sao Paulo International Airport/ Guarulhos (AISP/GRU) airfield based on simulation model. Different airport physical and operational scenarios had been analyzed by means of simulation. SIMMOD Plus 4.0, the computational tool developed to represent aircraft operation in the airspace and airside of airports, was used to perform these analysis. The study was mainly focused on aircraft operations on ground, at the airport runway, taxi-lanes and aprons. The visualization of the operations with increasing demand facilitated the analyses. The results generated in this work certify the viability of the methodology, they also indicated the solutions capable to solve the delay problem by travel time analysis, thus diminishing the costs for users mainly airport authority. It also indicated alternatives for airport operations, assisting the decision-making process and in the appropriate timing of the proposed changes in the existing infrastructure.

Santana, Erico Soriano Martins↗
NASA NTRS2006

Airport Surface Network Architecture Definition

Currently, airport surface communications are fragmented across multiple types of systems. These communication systems for airport operations at most airports today are based dedicated and separate architectures that cannot support system-wide interoperability and information sharing. The requirements placed upon the Communications, Navigation, and Surveillance (CNS) systems in airports are rapidly growing and integration is urgently needed if the future vision of the National Airspace System (NAS) and the Next Generation Air Transportation System (NGATS) 2025 concept are to be realized. To address this and other problems such as airport surface congestion, the Space Based Technologies Project s Surface ICNS Network Architecture team at NASA Glenn Research Center has assessed airport surface communications requirements, analyzed existing and future surface applications, and defined a set of architecture functions that will help design a scalable, reliable and flexible surface network architecture to meet the current and future needs of airport operations. This paper describes the systems approach or methodology to networking that was employed to assess airport surface communications requirements, analyze applications, and to define the surface network architecture functions as the building blocks or components of the network. The systems approach used for defining these functions is relatively new to networking. It is viewing the surface network, along with its environment (everything that the surface network interacts with or impacts), as a system. Associated with this system are sets of services that are offered by the network to the rest of the system. Therefore, the surface network is considered as part of the larger system (such as the NAS), with interactions and dependencies between the surface network and its users, applications, and devices. The surface network architecture includes components such as addressing/routing, network management, network performance and security.

Nguyen, Thanh C.↗
NASA NTRS2006

Airport Remote Tower Sensor Systems

Networks of video cameras, meteorological sensors, and ancillary electronic equipment are under development in collaboration among NASA Ames Research Center, the Federal Aviation Administration (FAA), and the National Oceanic Atmospheric Administration (NOAA). These networks are to be established at and near airports to provide real-time information on local weather conditions that affect aircraft approaches and landings. The prototype network is an airport-approach-zone camera system (AAZCS), which has been deployed at San Francisco International Airport (SFO) and San Carlos Airport (SQL). The AAZCS includes remotely controlled color video cameras located on top of SFO and SQL air-traffic control towers. The cameras are controlled by the NOAA Center Weather Service Unit located at the Oakland Air Route Traffic Control Center and are accessible via a secure Web site. The AAZCS cameras can be zoomed and can be panned and tilted to cover a field of view 220 wide. The NOAA observer can see the sky condition as it is changing, thereby making possible a real-time evaluation of the conditions along the approach zones of SFO and SQL. The next-generation network, denoted a remote tower sensor system (RTSS), will soon be deployed at the Half Moon Bay Airport and a version of it will eventually be deployed at Los Angeles International Airport. In addition to remote control of video cameras via secure Web links, the RTSS offers realtime weather observations, remote sensing, portability, and a capability for deployment at remote and uninhabited sites. The RTSS can be used at airports that lack control towers, as well as at major airport hubs, to provide synthetic augmentation of vision for both local and remote operations under what would otherwise be conditions of low or even zero visibility.

Maluf, David A.↗
NASA NTRS2016

Methods for Determining Aircraft Surface State at Lesser-Equipped Airports

Tactical departure scheduling within a terminal airspace must accommodate a wide spectrum of surveillance and communication capabilities at multiple airports. The success of such a scheduler is highly dependent upon the knowledge of a departure's state while it is still on the surface. Airports within a common Terminal RAdar CONtrol (TRACON) airspace possess varying levels of surface surveillance infrastructure which directly impacts uncertainties in wheels-off times. Large airports have access to surface surveillance data, which is shared with the TRACON, while lesser-equipped airports still rely solely on controllers in Air Traffic Control Towers (Towers). Coordination between TRACON and Towers can be greatly enhanced when the TRACON controller has access to the surface surveillance and the associated decision-support tools at well-equipped airports. Similar coordination at lesser-equipped airports is still based on verbal communications. This paper investigates possible methods to reduce the uncertainty in wheels-off time predictions at the lesser-equipped airports through the novel use of Over-the-Air (OTA) data transmissions. We also discuss the methods and equipment used to collect sample data at lesser-equipped airports within a large US TRACON, as well as the data evaluation to determine if meaningful information can be extracted from it.

departure scheduling↗
DOE OSTI2026

Estimating the Benefits of Sustainable Aviation Fuel Usage at Chicago O’Hare International Airport on Ultrafine Particle Exposure and Mortalities Reductions

The expanding commercial aviation sector necessitates diverse energy sources, and sustainable aviation fuels (SAFs) have emerged as a promising option. Widespread SAF adoption can help meet transportation fuel demand and offer health benefits for people residing near airports or along airport landing and takeoff (LTO) pathways, where elevated levels of aircraft-derived air pollution often exist. Blending SAF with traditional jet fuels can reduce ultrafine particle (UFP) emissions, which may improve health of near airport population. We analyzed a population of about 8 million people in 1925 census tracts around the Chicago O’Hare International Airport (ORD). We conducted a risk assessment to estimate anticipated UFP reductions for three adoption scenarios using blends of traditional jet fuels with 5, 25, and 50% SAF across all flights landing and taking off from ORD. We calculated baseline estimates of UFP emissions using ORD flight data, a dispersion model, and a calibration function derived from mobile monitoring data. We used this baseline UFP emission profile across the study area to estimate population-weighted UFP, as well as the attributable case reductions (ACRs) and attributable mortality rate reductions (AMRRs) across the demographic distribution around the airport, based on the SAF blending scenarios. We found a positive association of SAF blending with UFP reductions, particularly near the airport and along LTO flight pathways. Our study showed that the population-weighted UFP across different demographics was similar. ACRs were largely dependent on individual demographic populations, while AMRRs for all populations were relatively similar, with an estimated 0.3 (95% range: 0.2−0.3), 1.1 (0.9−1.4), and 1.8 (1.5−2.2) fewer mortalities per 100,000 people per year expected with the adoption of 5, 25, and 50% SAF blends, respectively. This study indicates that communities near ORD, across a range of demographics, may benefit similarly from SAF adoption, thus highlighting how SAF adoption may offer an opportunity to improve health outcomes like aviation UFP-related mortalities around airports.

10 SYNTHETIC FUELS↗
NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗
NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.

airport surface operations↗
NASA NTRS1998

Airport Financing and User Charge Systems in the USA

This paper examines the financing of U.S. public airports in a turbulent era of change, and projects toward the future. It begins by briefly outlining historical patterns that have changed the industry, and airport facilities in particular. It then develops basic principles of public finance as applied to public infrastructure, followed by the applicable principles of management. Following that, the current airport financing system is analyzed and contrasted with a socially optimal financing system. A concluding section suggests policy reforms and their likely benefits. The principles of finance and management discussed here are elementary. However, their implications are radical for U.S. airport policy. There is a great deal of room to improve the allocation of aviation infrastructure resources. The application of these basic principles makes it evident that in many cases, current practice is wasteful, environmentally unsound, overly costly, and inequitable. Future investments in public aviation capital will continue to be wasteful until more efficient pricing systems are instituted. Thus, problem in the U.S. is not one of insufficient investment in airport infrastructure, but investment in the wrong types of infrastructure. In the U.S., the vast majority of publically-owned airports are owned by local governments. Thus, while the federal government bad a great deal of influence in financing airports, ultimately these are local decisions. The same is true with many other public infrastructure issues. Katz and Herman (1997) report that in 1995, U.S. net public capital stock equaled almost $4.6 trillion, 72% of which ($3.9 trillion) was owned by state and local governments, most of it in buildings, highways, Streets, sewer systems, and water supply facilities. Thus, public infrastructure finance is fundamentally a local government issue, with implications for federal and state governments in the design of their aid programs.

Bartle, John R.↗
NASA NTRS1997

Surface Development and Test Facility (SDTF) New R&D Simulator for Airport Operations

A new simulator, the Surface Development and Test Facility (SDTF) is under construction at the NASA Ames Research Center in Mountain View, California. Jointly funded by the FAA (Federal Aviation Administration) and NASA, the SDTF will be a testbed for airport surface automation technologies of the future. The SDTF will be operational in the third quarter of 1998. The SDTF will combine a virtual tower with simulated ground operations to allow evaluation of new technologies for safety, effectiveness, reliability, and cost benefit. The full-scale level V tower will provide a seamless 360 degree high resolution out-the-window view, and a full complement of ATC (air traffic control) controller positions. The imaging system will be generated by two fully-configured Silicon Graphics Onyx Infinite Reality computers, and will support surface movement of up to 200 aircraft and ground vehicles. The controller positions, displays and consoles can be completely reconfigured to match the unique layout of any individual airport tower. Dedicated areas will accommodate pseudo-airport ramp controllers, pseudo-airport operators, and pseudo-pilots. Up to 33 total personnel positions will be able to participate in simultaneous operational scenarios. A realistic voice communication infrastructure will emulate the intercom and telephone communications of a real airport tower. Multi-channel audio and video recording and a sophisticated data acquisition system will support a wide variety of research and development areas, such as evaluation of automation tools for surface operations, human factors studies, integration of terminal area and airport technologies, and studies of potential airport physical and procedural modifications.

Dorighi, Nancy S.↗