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NASA NTRS2001

Airport Remote Tower Sensor Systems

Remote Tower Sensor Systems (RTSS) are proof-of-concept prototypes being developed by NASA/Ames Research Center (NASA/ARC) with collaboration with the FAA (Federal Aviation Administration) and NOAA (National Oceanic Atmospheric Administration). RTSS began with the deployment of an Airport Approach Zone Camera System that includes real-time weather observations at San Francisco International Airport. The goal of this research is to develop, deploy, and demonstrate remotely operated cameras and sensors at several major airport hubs and un-towered airports. RTSS can provide real-time weather observations of airport approach zone. RTSS will integrate and test airport sensor packages that will allow remote access to realtime airport conditions and aircraft status.

Papasin, Richard↗

NASA NTRS2003

Convenient Airports: Point of View of the Passengers

The competition among airlines or among airports aiming at to increase the demand for its services has been more and more incited. Knowledge the perception of the users for the offered services means to meet the customer's needs and expectations in order either to keep the customer, and therefore keep a significant advantage over competitors. The passenger of the air transportation wants rapidity, security and convenience. Convenience can be translated by comfort that the passenger wants for the price that he can pay. In this paper had been identified. as a result of a survey achieved in six Brazilian airports during 2002, the best indicators in the passenger's perception. These indicators among any others were listed m the handbook of Airports Council International (ACI). Distinctive perceptions were observed among passengers with different travel motivations. This survey had been carried through in the airports of Brasilia, Porto Alegre, Salvador. Fortaleza, Curitiba and Bel6m. Considering this survey we can identified the most attractive airport among them. This work is a way to help improve quality of service, in particular, m these six airports of the Brazilian network. The results should be published and made available to all the parties concerned (airport authority, airlines and service providers) and should lead to corrective action when the passenger is not satisfied with the service.

Magri, Adival Aparecido, Jr.↗

NASA NTRS2003

Cooperation of German Airports in Europe: Comparison of Different Types by Means of an Interdependence-Profile-Model

The limited growth possibilities in the home markets - not the least of which, based on capacity and expansion problems - force the large airport operators to enter into, via partnerships, cooperations and alliances. The German airports already cooperate among one another in different forms. The purpose of the paper is to examine the structures and possibilities of cooperation among airports in Europe (e.g. Airport Systems, Airport Networks). The experience of German airports with different cooperations and alliances will be also considered. Finally the forms of cooperations among airports are analysed by means of interdependence-profile-models with different features (mutual dependence, coordination volume, complexity, cooperation profit, value, degree of formalization and temporal frame), in order to find out how high the cooperative attachment of cooperation is to be evaluated.

Meincke, Peter A.↗

NASA NTRS2003

Determination and Applications of Environmental Costs at Different Sized Airports: Aircraft Noise and Engine Emissions

With the increasing trend of charging for externalities and the aim of encouraging the sustainable development of the air transport industry, there is a need to evaluate the social costs of these undesirable side effects, mainly aircraft noise and engine emissions, for different airports. The aircraft noise and engine emissions social costs are calculated in monetary terms for five different airports, ranging from hub airports to small regional airports. The number of residences within different levels of airport noise contours and the aircraft noise classifications are the main determinants for accessing aircraft noise social costs. Whist, based on the damages of different engine pollutants on the human health, vegetation, materials, aquatic ecosystem and climate, the aircraft engine emissions social costs vary from engine types to aircraft categories. The results indicate that the relationship appears to be curvilinear between environmental costs and the traffic volume of an airport. The results and methodology of environmental cost calculation could input for to the proposed European wide harmonized noise charges as well as the social cost benefit analysis of airports.

Lu, Cherie↗

NASA NTRS2005

Study Results on Knowledge Requirements for Entry-Level Airport Operations and Management Personnel

This paper identifies important topical knowledge areas required of individuals employed in airport operations and management positions. A total of 116 airport managers and airfield operations personnel responded to a survey that sought to identify the importance of various subject matter for entry level airport operations personnel. The results from this study add to the body of research on aviation management curriculum development and can be used to better develop university curriculum and supplemental training focused on airport management and operations. Recommendations are made for specialized airport courses within aviation management programs. Further, this study identifies for job seekers or individuals employed in entry level positions those knowledge requirements deemed important by airport managers and operations personnel at different sized airports.

Quilty, Stephen M.↗

NASA NTRS2008

Effect of Surface Traffic Count on Taxi Time at Dallas-Fort Worth (DFW) International Airport

As the amount of air traffic increases over the years, most airports simply do not have the means of expanding to handle the intensified traffic on the surface that will ensue. Precise surveillance equipment and automation concepts, as well as advanced surface traffic algorithms are being developed to improve airport efficiency. These surface algorithms require inputs unique to each airport to ensure maximum efficiency, and minimal taxi delay. This study analyzes surface traffic at Dallas-Fort Worth International Airport (DFW) to determine the effect of the number of aircraft on the surface and the amount of stop and go situations they experience to the amount of additional taxi time encountered. If the surface capacity of an airport is known, minimal delay can be accomplished by limiting the number of taxiing aircraft to that capacity. This concept is related to highways, where traffic flow drastically decreases as more cars occupy the road. An attempt to minimize this effect on highways is seen with the use of metering lights at freeway on-ramps. Since the surface traffic at airports is highly regulated, and aircraft are less mobile on the ground, limiting the surface count to a certain number can greatly reduce the amount of additional taxi time encountered, as well as reduce hazardous emissions. This study will also find the regions of an airport that encounter the most additional taxi time when the number of aircraft in that area is increased. This could help surface traffic algorithms avoid congesting that area, or re-route aircraft to different runways when that area reaches its capacity. The relationship between the amount of stop and go situations an aircraft encounters and their effect on the taxi time of that aircraft will also be investigated. This will help to determine the effect of holding an aircraft on the taxiway as opposed to re-routing it. The lesser of the two should be used when developing surface traffic algorithms to further minimize the delay encountered. The fields investigated in this study include taxi time, the number of aircraft on the surface, the number of stop and go situations, and the time stopped for each aircraft. Taxi time is defined as spot to runway for departures, and runway to spot for arrivals. It does not include ramp area taxi time because the ramp area is controlled differently, and surface traffic schedulers do not currently incorporate them. Taxi time is found by finding the difference between take-off time (OFF) and spot crossing time for departures, and spot crossing time and landing time (ON) for arrivals. All surface data was either found directly using the Surface Operations Data Analysis and Adaptation (SODAA), a tool to analyze the Surface Management System (SMS) generated log files, or indirectly from SODAA using Matlab to derive values from SODAA data. The number of aircraft on the surface is found by looping through the ON times, OFF times, and spot times for each aircraft during a particular day. For each departure aircraft, surface counts are taken at its spot crossing and OFF time. The average of these two is used as the surface count for that aircraft. For arrivals, surface counts are taken at its ON time and its spot crossing time. The average of these two is used.

Kistler, Matthew Stephen↗

NASA NTRS2011

Examining Passenger Flow Choke Points at Airports Using Discrete Event Simulation

The movement of passengers through an airport quickly, safely, and efficiently is the main function of the various checkpoints (check-in, security. etc) found in airports. Human error combined with other breakdowns in the complex system of the airport can disrupt passenger flow through the airport leading to lengthy waiting times, missing luggage and missed flights. In this paper we present a model of passenger flow through an airport using discrete event simulation that will provide a closer look into the possible reasons for breakdowns and their implications for passenger flow. The simulation is based on data collected at Norfolk International Airport (ORF). The primary goal of this simulation is to present ways to optimize the work force to keep passenger flow smooth even during peak travel times and for emergency preparedness at ORF in case of adverse events. In this simulation we ran three different scenarios: real world, increased check-in stations, and multiple waiting lines. Increased check-in stations increased waiting time and instantaneous utilization. while the multiple waiting lines decreased both the waiting time and instantaneous utilization. This simulation was able to show how different changes affected the passenger flow through the airport.

Brown, Jeremy R.↗

NASA NTRS2014

Predicting Ground Delay Program at an Airport Based on Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

traffic flow management↗

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.

Lee, Hanbong↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗

NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗

DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1985

Data on noise environments at different times of day around airports

Sources of information about noise environments at different times of the day at civilian and military airports are identified. Information about movements of scheduled flights are available in machine readable form from the Official Airline Guide. Information about permanent noise monitoring sites is readily obtained from individual airports. Limited data on the timing of flights are available at centralized locations for military airports. An examination of scheduled flights at commercial airports leads to the conclusion that differences between daytime and nighttime noise levels (measured in Equivalent Continuous Noise Level, LEQ) vary from 7 to 15 decibels. Data from 128 permanent noise monitoring sites at 11 airports are also examined. Differences between daytime and nighttime noise levels at these 128 noise monitoring sites vary from 3 to 17 decibels (LEQ). Preliminary analyses suggest that accurate estimates of time-of-day weights could not be obtained from conventional social surveys at existing airports.

Fields, J. M.↗

NASA NTRS1995

Noise-Induced Sleep Disturbance in Residences Near Two Civil Airports

A large-scale field study of noise-induced sleep disturbance was conducted in the vicinities of Stapleton International Airport (DEN) and Denver International Airport (DIA) in anticipation of the closure of the former and opening of the latter. Both indoor and outdoor measurements of aircraft and other nighttime noises were made during four time periods. Measurements were made in 57 homes located as close as feasible to the runway ends of the two airports. Sleep disturbance was measured by several indices of behaviorally confirmed awakening (button pushes upon awakening) and body movement (as measured with wrist-worn actimeters). A total of 2717 subject-nights of observations were made over the course of the study. Although average noise event levels measured outdoors decreased markedly at DEN after closure of the airport and increased slightly at DIA after its opening, indoor noise event levels varied much less in homes near both airports. No large differences were observed in noise-induced sleep disturbance at either airport. Indoor sound exposure levels of noise events were, however, closely related to and good predictors of actimetrically defined motility and arousal.

Fidell, Sanford↗

NASA NTRS2004

Wireless Channel Characterization in the Airport Surface Environment

Given the anticipated increase in air traffic in the coming years, modernization of the National Airspace System (NAS) is a necessity. Part of this modernization effort will include updating current communication, navigation, and surveillance (CNS) systems to deal with the increased traffic as well as developing advanced CNS technologies for the systems. An example of such technology is the integrated CNS (ICNS) network being developed by the Advanced CNS Architecture and Systems Technology (ACAST) group for use in the airport surface environment. The ICNS network would be used to convey voice/data between users in a secure and reliable manner. The current surface system only supports voice and does so through an obsolete physical infrastructure. The old system is vulnerable to outages and costly to maintain. The proposed ICNS network will include a wireless radio link. To ensure optimal performance, a thorough and accurate characterization of the channel across which the link would operate is necessary. The channel is the path the signal takes from the transmitter to the receiver and is prone to various forms of interference. Channel characterization involves a combination of analysis, simulation, and measurement. My work this summer was divided into four tasks. The first task required compiling and reviewing reference material that dealt with the characterization and modeling of aeronautical channels. The second task involved developing a systematic approach that could be used to group airports into classes, e.g. small airfields, medium airports, large open airports, large cluttered airports, etc. The third task consisted of implementing computer simulations of existing channel models. The fourth task entailed measuring possible interference sources in the airport surface environment via a spectrum analyzer.

Neville, Joshua T.↗

NASA NTRS2009

Initial Investigation of Operational Concept Elements for NASA's NextGen-Airportal Project Research

The NextGen-Airportal Project is organized into three research focus areas: Safe and Efficient Surface Operations, Coordinated Arrival/Departure Operations Management, and Airportal Transition and Integration Management. The content in this document was derived from an examination of constraints and problems at airports for accommodating future increases in air traffic, and from an examination of capabilities envisioned for NextGen. The concepts are organized around categories of constraints and problems and therefore do not precisely match, but generally reflect, the research focus areas. The concepts provide a framework for defining and coordinating research activities that are, and will be, conducted by the NextGen-Airportal Project. The concepts will help the research activities function as an integrated set focused on future needs for airport operations and will aid aligning the research activities with NextGen key capabilities. The concepts are presented as concept elements with more detailed sub-elements under each concept element. For each concept element, the following topics are discussed: constraints and problems being addressed, benefit descriptions, required technology and infrastructure, and an initial list of potential research topics. Concept content will be updated and more detail added as the research progresses. The concepts are focused on enhancing airportal capacity and efficiency in a timeframe 20 to 25 years in the future, which is similar to NextGen's timeframe.

Lohr, Gary↗

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 NTRS2011

C-Band Airport Surface Communications System Standards Development. Phase II Final Report. Volume 1: Concepts of Use, Initial System Requirements, Architecture, and AeroMACS Design Considerations

This report is provided as part of ITT s NASA Glenn Research Center Aerospace Communication Systems Technical Support (ACSTS) contract NNC05CA85C, Task 7: New ATM Requirements-Future Communications, C-Band and L-Band Communications Standard Development and was based on direction provided by FAA project-level agreements for New ATM Requirements-Future Communications. Task 7 included two subtasks. Subtask 7-1 addressed C-band (5091- to 5150-MHz) airport surface data communications standards development, systems engineering, test bed and prototype development, and tests and demonstrations to establish operational capability for the Aeronautical Mobile Airport Communications System (AeroMACS). Subtask 7-2 focused on systems engineering and development support of the L-band digital aeronautical communications system (L-DACS). Subtask 7-1 consisted of two phases. Phase I included development of AeroMACS concepts of use, requirements, architecture, and initial high-level safety risk assessment. Phase II builds on Phase I results and is presented in two volumes. Volume I (this document) is devoted to concepts of use, system requirements, and architecture, including AeroMACS design considerations. Volume II describes an AeroMACS prototype evaluation and presents final AeroMACS recommendations. This report also describes airport categorization and channelization methodologies. The purposes of the airport categorization task were (1) to facilitate initial AeroMACS architecture designs and enable budgetary projections by creating a set of airport categories based on common airport characteristics and design objectives, and (2) to offer high-level guidance to potential AeroMACS technology and policy development sponsors and service providers. A channelization plan methodology was developed because a common global methodology is needed to assure seamless interoperability among diverse AeroMACS services potentially supplied by multiple service providers.

Hall, Edward↗