Individual Airports Research Articles

Adopted LTO cycle to operational conditions at polish airports

The aim of this article is to take into account specifications of the airport area in LTO test, when impact of the air transport at the airport- proximate area is analysed. The LTO test doesn’t con-sider the specifications of e.g. airport manoeuvring area and length of taxiways. The LTO test calculation methodology requires knowledge of aircraft engine parameters, such as thrust, spe-cific fuel consumption and emission values indexes. Based on the data contained in the Engine Emissions Database (ICAO), the parameters of the LTO test can be clearly determined. The main analysis element was to conduct a taxiing simulation using the CKAS MotionSim5 Flight Simu-lator in order to indicate the operational taxiing time at individual polish airports. Statistically significant differences were found between the emissions of selected exhaust gas components in the regulation LTO test and the operational LTO test. Analyzes were carried out in relation to three different propulsions that are most often used by carriers serving the largest number of passengers in Poland.

Modeling the Propagation of Infectious Diseases across the Air Transport Network: A Bayesian Approach

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology.

How representative are air transport functional complex networks? A quantitative validation.

Functional networks have emerged as powerful instruments to characterize the propagation of information in complex systems, with applications ranging from neuroscience to climate and air transport. In spite of their success, reliable methods for validating the resulting structures are still missing, forcing the community to resort to expert knowledge or simplified models of the system's dynamics. We here propose the use of a real-world problem, involving the reconstruction of the structure of flights in the US air transport system from the activity of individual airports, as a way to explore the limits of such an approach. While the true connectivity is known and is, therefore, possible to provide a quantitative benchmark, this problem presents challenges commonly found in other fields, including the presence of non-stationarities and observational noise, and the limitedness of available time series. We explore the impact of elements like the specific functional metric employed, the way of detrending the time series, or the size of the reconstructed system and discuss how the conclusions here drawn could have implications for similar analyses in neuroscience.

Understanding the impact of network structure on air travel pattern at different scales.

This study examines the global air travel demand pattern using complex network analysis. Using the data for the top 50 airports based on passenger volume rankings, we investigate the relationship between network measures of nodes (airports) in the global flight network and their passenger volume. The analysis explores the network measures at various spatial scales, from individual airports to metropolitan areas and countries. Different attributes, such as flight route length and the number of airlines, are considered in the analysis. Certain attributes are found to be more relevant than others, and specific network measure models are found to better capture the dynamics of global air travel demand than others. Among the models, PageRank is found to be the most correlated with total passenger volume. Moreover, distance-based measures perform worse than the ones emphasising the number of airlines, particularly those counting the number of airlines operating a route, including codeshare. Using the PageRank score weighted by the number of airlines, we find that airports in Asian cities tend to have more traffic than expected, while European and North American airports have the potential to attract more passenger volume given their connectivity pattern. Additionally, we combine the network measures with socio-economic variables such as population and GDP to show that the network measures could greatly augment the traditional approaches to modelling and predicting air travel demand. We'll also briefly discuss the implications of the findings in this study for airport planning and airline industry strategy.

A novel intelligent approach for flight delay prediction

Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.

Обоснованность регулирования сборов и тарифов на услуги в аэропортах

The article examines the problem of the validity of regulation of airport services’ fees and tariffs. The study is relevant amid criticism of the need for regulation in the context of commercialization of the industry given the disputes over natural monopoly’ status of airports, potential competition between airports and the viability of alternative transaction governance mechanisms. The objective of the research is to analyze possible justifications for the necessity of airport services regulation and to reveal limitations of each of justifications. The analysis is carried out within the framework of two approaches: the natural monopoly model of the theory of industry organization and a comparative analysis of discrete structural alternatives from the new institutional economic theory. The following results were obtained. Firstly, the research shows that the necessity of regulation within the framework of these approaches is justified by the presence of two risks – the risk of abuse of a dominant position and the risk of expropriation of investments made by consumers of airport services. Secondly, concerning the limitations of the justifications, the article systematizes the factors contributing to the strengthening or weakening of the market power of airports. Thirdly, the research identifies the characteristics of airports that are subject to examination when examining the need of for regulation. Fourth, it is demonstrated that the level of risk of expropriation of assets varies depending on the scale of the airport’s activities and the role of the airport in the route networks of airlines. Thus, the paper concludes that within the framework of each of the approaches considered, the decision to introduce regulation of services at airports should be made not for the industry as a whole, but for individual airports, and shows the possible risks of a selective approach to regulating services at airports (the expansion of the regulator’s discretion, the increase in the regulator’s costs due to the need for analysis for the airports under consideration, corruption risks), which must be taken into account when designing relevant regulations if a decision is made to implement the proposed approach to regulation.

Optimal network-wide adjustments of initial airport slot allocations with connectivity and fairness objectives

Due to serious demand-supply imbalances, many airports around the world are highly congested. Access to these highly congested (Level 3, coordinated) airports is controlled through the use of the IATA World Airport Slot allocation Guidelines (WASG). At an individual airport, slots requested by each airline are allocated at the airport under consideration independently without taking into account the interactions between slots allocated at different airports. However, in order for the air-transport network to operate seamlessly, ensuring network-wide connectivity of flights, and the interdependencies existing between the slots allocated at individual airports need to be considered. Several models have been proposed in the literature to deal with the optimum allocation of slots at a single airport. However, the literature currently does not adequately address the network-wide slot allocation problem. In this paper, we are introducing a novel approach to address the network-wide slot allocation problem. Our approach considers as an input the individual airport schedules generated during the slot allocation process at individual airports and optimally adjusts them to ensure network-wide flight connectivity by taking into account the interdependencies existing between flights connecting pairs of airports.To this end, we propose bi-objective mathematical models, which consider schedule efficiency and inter-airline fairness objectives, and incorporate the importance that different airports have for the functioning of the air transport network, using the IATA connectivity indices and the betweenness centrality measures. We solve the proposed models using the ε − constraint method to investigate trade-offs between network-wide schedule efficiency and fairness, and we investigate the effect of these trade-offs on the airlines and the airports. Results from the application of the proposed models to a test network suggest that the consideration of the contribution of the airports to network connectivity affect the way that the total network-wide schedule displacement is distributed among the airports. Specifically, we found that the use of the IATA connectivity index tends to allocate less schedule displacement to airports with frequent flights to many destinations, while the use of the betweenness centrality measure allocates less schedule displacement to airports that are more critical in ensuring the connectivity of other airports in the network.

The role of airport costs in the post-9/11 recovery of San Francisco International Airport

This paper examines whether or to what extent San Francisco International Airport’s (SFO’s) purportedly high costs relative to other airports suppressed SFO’s traffic demand and prolonged the recovery of passenger traffic in the period after the 11th September, 2001 terrorist attacks. A comparative analysis of the historical data shows that SFO’s direct airport costs per enplanement (CPE) were higher than most other major airports in the post-9/11 period by a considerable margin, but that airport costs likely were not a significant factor in SFO’s prolonged recovery. Airport costs likely did not affect airline profitability sufficiently to influence route and scheduling decisions, and it is unlikely that passenger demand was dampened by the pass-through of higher costs to airfares. This research suggests at best a tenuous relationship between airport rates and passenger activity, and at SFO the evidence does not suggest that an extremely high CPE relative to peer airports affected the pace of its slow return to pre-9/11 traffic levels. Rather, the slow return of traffic to SFO and its neighbour, Norman Y. Mineta San Jose International Airport (SJC), compared to the robust growth at Oakland International Airport (OAK) can be explained by traditional economic means. It also suggests that in a multi-airport region where each airport is somewhat specialised, an individual airport’s over- or underperformance relative to its neighbours is more likely to be a function of economic geography than of any airport’s rate-setting practices. If this conclusion holds true for other US airports, it emphasises the ability of airports to create capital plans and set rates and charges to make long-term investments in organic air traffic growth, ensuring that new entrant airlines have sufficient facilities to begin service and that existing airlines have sufficient capacity to grow.

Navigating digital transformation in airports : A disciplined approach to project portfolio selection and execution

Airports are undertaking digital transformation initiatives to achieve operations excellence, improve sustainability and enhance competitiveness. While digital transformation offers many potential benefits for airports, including improved insights, efficiencies, streamlined passenger journeys and new business/ revenue opportunities, there are risks associated with not utilising a disciplined approach when building a digital transformation project portfolio. Just as all airports are not the same, digital transformation initiatives are not ‘one size fits all’. Projects to introduce new systems and deploy new technology under the banner of digital transformation need to be in support of the strategic goals of the airport and in alignment with business and operational objectives. Digital transformation initiatives must also fit within the resource and skills capacity of the airport to successfully perform the work and provide the necessary post-deployment support afterwards. This paper posits that a disciplined and analytical approach to evaluating both the likely effort required for successful execution and the potential impact and value generated can assist airports with the selection and timing of projects, with the aim of avoiding ‘digital disappointment’. It concludes that digital transformation projects selected and sequenced on the basis of objective criteria are most likely to address the business and operational needs of individual airports and generate the most value for airport employees, stakeholders and passengers.

Impact of Airports on Landside Industrial Development: A Case Study of Brisbane Airport

Numerous studies have established strong connections between air traffic and economic growth at the regional level. However, without consideration of individual airports’ immediate vicinities, this aggregate view can lead to an overly simplistic conclusion about the role of airports in their urban environment. The impact of airports on local economic development is evaluated through a case study of Brisbane Airport by using primary data sourced from semi-structured interviews and a survey. The results demonstrate that the role of airports as transport hubs is not the dominant mechanism that drives industries to locate in airport areas. Rather, the local urban context is a dominant driver for development in and around airports. Based on the survey and interview results, urbanization economies have the highest significance on the landside industrial location decision, with a mean of 3.53 in terms of importance level. The findings provide a reference for policymakers regarding investment in airport expansion or construction, as well as a refinement of the understanding of the local economic impact of airports.

Monetary mapping of the climate footprint of air travel to a single airport

The annual climate costs of an individual airport are calculated accounting for its global rather than local environmental impact. By mapping the climate footprint of air travel to an airport instead of airlines, flights or passengers, the potential climate cost savings can be derived if all scheduled air services at an airport were to be suspended. The approach is used in an empirical case study for a medium-sized airport in Germany. The climate impact of scheduled air travel to and from this airport within a specified year is thus given a price tag that considers greenhouse gases beyond carbon dioxide. It is argued that the airport's climate costs are low compared to its economic benefits.

Changes in the volume of air traffic and specialization of segments of the Moscow Aviation Hub from 2014 to 2021

The object of this study is the changes in the development of the Moscow aviation hub in the second decade of the XXI century. The subject of the study is a statistical analysis of changes in the volume of air transportation of passengers, cargo and mail at the Moscow Aviation Hub from 2014 to 2021, both in general and for individual airports in comparison. The change in the share of Sheremetyevo, Domodedovo and Vnukovo in the total volume of the listed types of air transportation is analyzed. The dynamics of the ratio of domestic and international air transportation is investigated. The share of the Moscow aviation hub in the total volume of passenger traffic, cargo turnover and postal air transportation in Russia is considered. The research is based on statistical, historical-geographical, problem-analytical methods, as well as the method of system-structural analysis. This made it possible to conduct an objective, scientifically-based analysis of the processes of changing the volume of air traffic in the Moscow aviation hub. The main conclusions of the study are: air transportation of passengers at the Moscow Aviation hub decreased from 2014 to 2021, while air transportation of cargo and mail increased during this period. The share of Sheremetyevo Airport in all respects increased, the share of Domodedovo decreased, the share of Vnukovo either grew or remained unchanged. The main contribution of the authors to the study of the topic is to consider changes in the statistical parameters of the Moscow aviation hub airports in the context of socio-economic and geopolitical processes, in historical and geographical conditionality; in identifying the key factors of the current dynamics. The novelty of the study is to identify the main trends in the development of the volume and structure of air transportation of the Moscow aviation hub from 2014 to 2021 under the conditions of sanctions pressure and restrictions associated with the COVID-19 pandemic.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%–98.4%) as well as the number of flights (1.5%–82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Air delay propagation patterns in Europe from 2015 to 2018: an information processing perspective

The characterisation of delay propagation is one of the major topics of research in air transport management, due to its negative effects on the cost-efficiency, safety and environmental impact of this transportation mode. While most research works have naturally framed it as a transportation process, the successful application of network theory in neuroscience suggests a complementary approach, based on describing delay propagation as a form of information processing. This allows reconstructing propagation patterns from the dynamics of the individual elements, i.e. from the evolution observed at individual airports, without the need of additional a priori information. We here apply this framework to the analysis of delay propagation in the European airspace between 2015 and 2018, describe the evolution of the observed structure, and identify the role of individual airports in it. We further use this analysis to illustrate the limitations and challenges associated to this approach, and to sketch a roadmap of future research in this evolving topic.

Quantitative method for resilience assessment framework of airport network during COVID-19

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

Examining COVID-19-triggered changes in spatial connectivity patterns in the European air transport network up to June 2021

The integrity of international supply chain operations heavily relies on air transport services to facilitate the movement of goods and enable human interactions between its stakeholders. With the outbreak of COVID-19 in Europe around March 2020, air transport networks have been subject to profound alterations. Although the link between variations in air transport service levels and changes in user costs for network-wide travel has been analysed extensively, few studies have examined the extent to which severe network shrinkage events lead to a reduction in network connectivity, which is therefore difficult to predict. This paper investigates how the COVID-19 pandemic has structurally altered the European air transport network in 2020/21 and how these changes have deteriorated users' ease when utilising network-wide air transport services. To do this, the paper estimates the change in average quickest path length at the airport level during different stages of this period. Results indicate there is strong heterogeneity in airports' susceptibility to pandemic-induced network changes, with both regional variations and variations in the airline type serving individual airports. Furthermore, topological features of individual airports are found to determine airport susceptibility. The findings are discussed in terms of their implications for locational decisions in supply chain designs.

Productivity Analysis of Major Airports in Korea Using Malmquist Productivity Indices

Purpose - The purpose of this paper is to analyze the productivity of major airports in Korea using Malmquist Productivity Index methodology and draw implications for airport operations and planning through comparison with existing research results. Also, The results of this study confirm the academic differences between productivity and efficiency. The results of this study provide policy assistance such as attracting, expanding and relocating airports, and eliciting academic implications. Design/Methodology/Approach - We analyzed the productivity of airports in Korea by applying the Malmquist Productivity Index Analysis Methodology among DEA methodologies. Not only individual airports, but also group-specific airports were analyzed. In addition, we compared the results of airport efficiency studies analyzed using efficiency analysis methods such as CCR/BCC. Findings - The larger the airport, the more productive it is. The productivity of the base airport (large) and the productivity of the general airport (medium) and the general airport (small) is inversely proportional. These results resulted in significant differences from existing efficiency studies. For example, an airport with very low efficiency can be measured very high in productivity with only a little performance. In general, however, highly efficient airports also tended to have high productivity, which means there is a correlation between efficiency and productivity. Research Implications - This study identified the relationship between efficiency and productivity by studying productivity aspects, not efficiency. As a result, productivity analysis as well as efficiency analysis should be accompanied for proper airport evaluation. As a result of this study will help not only the operation of the airport but also the planning and expansion of the airport.

Bird strikes at commercial airports explained by citizen science and weather radar data

Abstract Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict the occurrence of birds vary extensively in space and time at the local scales of airport responses. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large‐scale bird survey methods. As a test case, we compare results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports. We show that weather radar‐based estimates of migration intensity can accurately predict the probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird‐based estimates of species occurrence can, using species’ body mass and flocking propensity, accurately predict when most damaging strikes occur. Synthesis and applications. By better understanding when and where different bird species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur. Our results highlight the power of federating datasets with bird movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes.

US Federal Aviation Administration Neighborhood Environmental Survey: Study Design and Survey Methodology

As part of the agency's broader noise research program, the Federal Aviation Administration (FAA) has undertaken a multi-year research effort to quantify the impacts of aircraft noise exposure on communities around commercial service airports in the United States (U.S.). The overall goal of the study was to produce an updated and nationally representative civil aircraft dose-response curve; providing the relationship between annoyance and aircraft noise exposure around U.S. airport communities. To meet this goal, the FAA sponsored a research team to help design and conduct a national survey, known as the Neighborhood Environmental Survey (NES). Residents from households around 20 airports were selected for participation in the survey; the detailed sampling process is covered in another paper. Two survey instruments were administered to adult residents within the NES: a mail questionnaire and a follow-up telephone interview for the mail respondents. The mail survey was administered to samples of individuals in the selected airport communities in six separate "waves" over a 12-month period starting in October 2015. All mail survey respondents were invited to complete a follow-up telephone interview, which asked detailed questions on several areas including respondents' opinions on noise, exposure to aircraft noise, relationship to the airport, concerns about aircraft operations, views on airport community relations, among others. Analysis of the survey responses on annoyance levels and the associated DNL was used to estimate dose-response curves for each individual airport and a national dose-response curve. The national dose-response curve created from the mail questionnaire shows considerably more people are highly annoyed by aircraft noise at a given noise exposure level compared to historical FICON data. This paper provides a detailed discussion of the survey design and methodology. Additional information describing the motivation to conduct the NES and how its findings will help inform ongoing work to address aircraft noise concerns; and the noise methodology are provided in companion papers.

U.S. Federal Aviation Administration Neighborhood Environmental Survey: Noise Methodology

As part of the agency's broader noise research program, the Federal Aviation Administration (FAA) has undertaken a multi-year research effort to quantify the impacts of aircraft noise exposure on communities around commercial service airports in the United States (U.S.). The overall goal of the study was to produce an updated and nationally representative civil aircraft dose-response curve; providing the relationship between annoyance and aircraft noise exposure around U.S. airport communities. To meet this goal, the FAA sponsored a research team to help design and conduct a national survey, known as the Neighborhood Environmental Survey (NES). A multi-stage and statistically rigorous process was used to select a representative sample of US airports, including a balanced sampling frame that included six factors: geographic distribution, temperature, day-night split, operational tempo, fleet mis, and population density. The research team computed Day-Night Average Sound Levels (DNL) for the 20 airports in two waves: first for survey respondent selection, and then for pairing with respondent locations to create the dose response curve. Analysis of the responses and the associated DNL was used to generate dose-response curves for each individual airport and a national dose-response curve showing the percent "Highly Annoyed" for a given noise level. The National dose-response curve created from the mail questionnaire shows considerably more people are highly annoyed by aircraft noise at a given noise exposure level compared to historical FICON data. This paper presents the airport selection and noise modeling process used to support the creation of the National dose-response curve. Additional information describing the motivation to conduct the NES and how its findings will help inform ongoing work to address aircraft noise concerns; and the survey methodology are provided in companion papers.