Major Airports Research Articles

Countering a Drone in a 3D Space: Analyzing Deep Reinforcement Learning Methods.

Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people's privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data.

Supply chain optimization of sustainable aviation fuel from carinata in the Southeastern United States

Carinata is a purpose-grown oilseed feedstock for renewable fuels, including sustainable aviation fuel (SAF) that can replace conventional aviation fuel (CAF). Given carinata is a new crop in the Southeastern United States, it is crucial to analyze its sustainability from a supply chain perspective. This study developed a mixed-integer linear programming (MILP) model and simulated it for 20 years, starting from a farm (county-level data) and ending at the airport. About 2.06 million ha in Alabama, Florida, and Georgia combined were found suitable for carinata production. Given the three-year rotation period, about 0.69 million hectares can be cultivated annually, approximately 65% of which was in Georgia. About 2.4% of the combined SAF annual demand of four major airports (about 210 thousand t) in the study area is satisfied at that level of carinata cultivation. However, all available SAF was supplied to the Atlanta airport as this decision minimizes the supply chain cost. A total of 1343 storage units, one oil extraction mill, and one biorefinery were needed to meet the overall demand. We found that SW Georgia is the top supplier of carinata seeds. The unit cost of production and carbon intensity were estimated to be $0.89 L-1 (or $26.79 GJ−1) and 0.91 kg CO2e L−1 (or 27.28 kg CO2e GJ−1), respectively. This carbon intensity of carinata-based SAF was 67.8% lower than that of CAF. With variations included in SAF demand, yield, and soil carbon sequestration, carbon savings remained between 66.5% and 67.8%. Given the GHG advantage of SAF over CAF, there is justification for subsidies required to make SAF competitive.

해외 특송 서비스의 위험요인 식별 및 관리 방안에 관한 연구

The growth of the e-commerce based on the development of ICT technology has grown more explosively due to the aftermath of social distancing caused by the spread of COVID-19. Because of the rapid increase of global express cargo since 2020, Global express service problems are detected from the major ports and airports in all countries. This phenomenon is expected to continue for the time being due to the proliferation of non-face-to-face transactions and an increase in diverse consumer demand. Global express service is the industry most affected by the delay of global logistics services, and various problems such as delay in delivery as well as sales of counterfeit goods, product damage, and tariffs are being pointed out. In particular, as the purchasing agency of overseas products spreads in domestic online shopping malls, not only consumers but also various entities that provide express services are faced with various problems. Accordingly, the Korean government is seeking various solutions, but there is a limitation in that it cannot take into account the characteristics of various entities participating in global express services. In this study, we define gobal express service as a standard process from the perspective of the global supply chain, and identify possible risk factors in 9 stages from the stage of ordering overseas products to receiving the goods after arriving at Incheon International Airport, the possible risk factors were identified in 9 stages to determine their relative importance. In addition, based on the survey results of those who have experienced direct purchases overseas and those involved in global express service, risk factors with high priority were compared and analyzed from the perspective of the participants. Based on this, the damage of the top 10 risk factors is divided into 4 categories : Reliability/Trust, Lead Time, Cost, and Quality/Safety. Countermeasures in terms of policy, system, and service operation were presented for each risk factor.

Presumptive Contamination: A New Approach to PFAS Contamination Based on Likely Sources.

While research and regulatory attention to per- and polyfluoroalkyl substances (PFAS) has increased exponentially in recent years, data are uneven and incomplete about the scale, scope, and severity of PFAS releases and resulting contamination in the United States. This paper argues that in the absence of high-quality testing data, PFAS contamination can be presumed around three types of facilities: (1) fluorinated aqueous film-forming foam (AFFF) discharge sites, (2) certain industrial facilities, and (3) sites related to PFAS-containing waste. While data are incomplete on all three types of presumptive PFAS contamination sites, we integrate available geocoded, nationwide data sets into a single map of presumptive contamination sites in the United States, identifying 57,412 sites of presumptive PFAS contamination: 49,145 industrial facilities, 4,255 wastewater treatment plants, 3,493 current or former military sites, and 519 major airports. This conceptual approach allows governments, industries, and communities to rapidly and systematically identify potential exposure sources.

Morning Transition of the Boundary Layer over Dallas–Fort Worth

Abstract Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city. Significance Statement Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.

Psychological Contract Fulfilment Mediating Role in the Relationship Between HRMP and Employee Performance: A Conceptual Paper

This study examines the influence of PCF towards the relationship between HRMP and employee performance at Libyan international airport. PCF is being neglected in the Libyan setting in general and in the LIA in particular. Therefore, the objective of this research is to recognize the significance of PCF in the advancement of EP and the employment of PCF in the HR department of LIA. Yet, the function of PCF in mediating between HRMP and EP remains unexamined. This research, therefore, focuses on the important role that PCF plays in the successful use of HRMP and the impact that it has on EP. Previous research has indicated that HRMP may be supported by PCF as a supporting tool. This study will adopt the quantitative approach. A survey method contains a cross-sectional design will be adopted as the research strategy to obtain data. The target population will be restricted to employee of the only major Libyan international airports functioning which are Tripoli, Benghazi, and Sabha. More specifically, the employees of all departments at Libyan international airports. The study is expected to have a significant relationship between its variables.

How do airlines react to slot displacements? Evidence from a major airport

The main strategic mechanism developed by the aviation industry to manage airport capacity is IATA's Slot Allocation Process. At airports where such mechanism is in place, airlines need to be assigned slots by an independent slot coordinator to schedule their flights. The slot allocation process involves multiple steps spanning several months of negotiations between airlines and slot coordinators. The most critical step is the initial slot allocation during which the coordinators communicate to airlines whether their slot requests have been accepted or need to be displaced (i.e., moved to a different time of the day). In this article, we use logistic regression models to analyze the reactions of airlines to slot displacement decisions by coordinators and the subsequent need to change their flight schedules. We focus our analysis on Paris Charles de Gaulle (CDG) Airport, one of the busiest worldwide. Our results indicate that even a small displacement (of less than 30 min) can decrease substantially the odds of a flight being scheduled, but that these odds vary widely with the type of airline involved, the time of the day and several other factors. This information could be used by coordinators to make initial slot allocation decisions that take into account their expected flight scheduling implications. Ultimately, this would contribute to more efficient use of the scarce capacity available at congested airports and increased responsiveness to the mobility needs of air passengers.

Impact of Aviation Emissions and its Changes Due to the COVID-19 Pandemic on Air Quality in South Korea

This study analyzed the impact of aviation emissions based on the 2017 CAPSS (Clean Air Policy Support System) data. We focused on major airports in South Korea and examined the concentration of NO2 and PM2.5 by the WRF-SMOKE-CMAQ modeling system. Furthermore, the number of flights in Korea greatly declined in response to the COVID-19 pandemic. To assess the impact of COVID-19 on aviation emissions, time resolution data were newly derived and air pollutant emissions for 2020 were calculated. Additional BAU (Business as Usual) emissions were calculated as well for comparison. Among airports in Korea, RKSI (Incheon International Airport) had the greatest impact on air quality in nearby areas. Changes in emissions due to COVID-19 showed a large deviation by airports for domestic emissions while international emissions had a consistent decrease. The reduced emissions had the strongest impact on air quality in the RKSI area as well. By analyzing aviation emissions due to COVID-19, this study confirmed the notable relationship with the pandemic and air quality. We conclusively recommend that policymakers and industry take note of trends in aviation emissions while establishing future atmospheric environment plans.

Aircraft noise and cardiovascular disease hospitalization and mortality near major airports in the United Kingdom

Aircraft noise and cardiovascular disease hospitalization and mortality near major airports in the United Kingdom

Travel mode choice for domestic intercity travel: A case study in Suzhou, China

This study examines how residents in Suzhou, China choose between different travel modes for domestic intercity travel. Suzhou provides an interesting case study because of its developed high-speed rail (HSR) network and proximity to three major airports. Semi-structured qualitative interviews were administered to 158 participants to obtain information about their most recent intercity trip and important factors for choosing travel modes. The interviews were thematically analysed, and participants were coded as being in or out of themes to allow for chi-squared tests of independence (to examine associations between themes and demographic variables) and binary logistic regressions (to predict travel mode choice based upon themes). The findings show that accessibility, convenience, and price increase the likelihood of a participant having chosen HSR. However, the more important contribution is methodological, highlighting the importance of studying actual behaviours (rather than attitudes and preferences) and avoiding the issues of self-generated validity and construct creation.

Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem

The major metropolitan airports in Australia have become increasingly congested, resulting in substantial increases in operational costs and waiting time at these airports. This paper assesses the effectiveness of subsidy programs in shifting airfreight from metropolitan airports to regional airports assuming the vehicle routing problem approach is used to optimise the downstream (i.e., road) logistics. We analyse the freight distribution network structure and logistics decisions under two (government) subsidy scenarios. We develop a mixed integer linear programming model incorporating the time-window and release-time constraints. A case study in Australia is used to illustrate the application of the proposed framework. The results show that introducing subsidies can effectively reduce the total costs from the prospective of industries involved in the airfreight distribution. The subsidy program under a non-linear subsidy provides a better performance from the economic and delivery time perspectives. However, if the primary goal is to reduce the volume of cargo traffic at the metropolitan airport, a linear subsidy program is preferred.

Is an NWP-Based Nowcasting System Suitable for Aviation Operations?

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.

Multi-Airport System Development Model: Case Study of Airports in Indonesia

Indonesia's air transportation business has grown substantially to suit the community's transportation needs. An increasing number of people travel by airline each year. By way of secondary and tertiary multiplier effects, the intensifying competition between airport services provided in neighboring regions is likely to have a multiplier influence on a territory. Simultaneously, airport services that are more competitive are focused on areas with rising economic growth in sectors like tourism. MAS is an airport system consisting of at least two airports within a metropolitan area that support civil aviation. MAS includes both big and small airports. MAS is the development of an air transportation system to suit the growing demand for air transportation services. As an example of an integrated multi-model airport design in Indonesia, this research will examine Juanda International Airport, Surabaya, as the major airport and Abdul Rachman Saleh Airport as the secondary airport. In order to establish an integrated multi-modal airport in Indonesia, it is necessary to adopt a multi-airport system. This study's airport location is in East Java Province and includes two airports: Juanda International Airport and Abdul Rachman Saleh Airport. The Juanda International Airport is situated in Sedati, Sidoarjo, whilst the Abdul Rachman Saleh Airport is in Pakis, Malang Regency. Using modelling findings and final passenger statistics, airport capacity in 2045 is determined. The results demonstrated the necessity for more comprehensive points in the MADAM simulation used in this research study, which can estimate a number of crucial parameters. Doi: 10.28991/CEJ-SP2021-07-013 Full Text: PDF

On Airport Time Slot Auctions: A Market Design Complying with the IATA Scheduling Guidelines

The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grants BI-1057/I-9 and 405008493]. M. Bichler gratefully acknowledges funding by the German National Science Foundation [DFG BI 1057/9-1].

Changes in Ultrafine Particle Concentrations near a Major Airport Following Reduced Transportation Activity during the COVID-19 Pandemic.

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017–9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020–6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)–51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

Air route development - Lessons from Australia

This paper aims to assess the uniqueness of stakeholder engagement activities in air route development (ARD) and identify what leadership and governance attributes are more relevant in the four critical stages of ARD. Our context is the Australian aviation market, analysing the differences in this process when developing routes to airports of different sizes. In-depth semi-structured interviews were used as the primary data collection tool. Twenty-two ARD experts from airports, airlines, and tourism authorities (TAs) shared their views on ARD and stakeholder engagement. Our results are presented using the four stages of ARD (i.e., development of route objectives, market research, route development activities, and route implementation) and key leadership and governance attributes identified in each of these stages. In a tourism sector heavily dependent on air transport, where Australian airports operate under a light-handed regime with little competition among major airports, we have identified strong partnerships between the involved stakeholders. While the airport often leads the ARD process, airports of different sizes engage differently. TAs play a significant role, particularly for regional and emerging airports. The results of this study analyse the ARD procedures and strategies used by airports, airlines, and TAs.

A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries.

Introduction:Coronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue.MethodsThis study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error.ResultsOur results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability.DiscussionThe proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies.

Australian Airports and Local Economic Development

ABSTRACT This article investigates the impact of airports on the surrounding landside industrial development by employing secondary data of nine major Australian airports captured from Google Maps using MapQuery. Previous studies suggest a strong connection between airports and economic development at the regional scale. However, few studies explore this link around the surrounding vicinity of the airport. This article provides new evidence on the link between airports and the local economy in the Australian context. The empirical results demonstrate that the size of landside industries is highly correlated with both airports’ passenger numbers and international air freight volume at the correlation coefficient of 0.912 and 0.871. Service sectors account for over 50 percent in the landside industries of most airports indicating a tendency of diversified and service-oriented landside development, which implies that airports are drivers of economic restructuring. The evidence confirms that airports have a positive impact on landside industrial development around airports, which align with the findings within this strand of airport literature.

METHODOLOGY OF THE STATUS ASSESSMENT OF AIR NETWORK

The air transport network is one of the most important and critical infrastructures in today's global economy. Air transport would not be able to operate without the air network. This fact significantly contributed to interest in a closer study of the issue of air network status assessment. The aim of this article is to evaluate the European air network by identifying and evaluating major European airports based on scheduled flights. Robustness of this air network was determined with regard to its evaluation. According to research, a suitable methodology was chosen which is describing the organization of data, specification of the research area and selection of method. Civil international airports which are located in European countries recognized by their entire size and handled at least 1 million passengers in 2018, were chosen as the basis for the creation of the European air network model and following evaluation. For these airports, individual scheduled flights were searched including their frequency during the selected period on 11-17 November 2019. The method of centrality used in the field of graph theory was chosen to evaluate the air network of these selected airports with regard to their connectivity. The obtained results identified the most important airports of the European air network and thus represent status assessment of air network during the winter flight schedule 2019/2020. These results were used to monitor the robustness of the European air network as a whole. The robustness of the air network was monitored based on a simulation of the sequent closure of the most important airports in the network with regard to the change of average path length and the size of the giant component. The European air network becomes inoperable when 19-20% of the best evaluated airports in the network are closed.

Corrupted bifractal features in finite uncorrelated power-law distributed data

Multifractal Detrended Fluctuation Analysis stands out as one of the most reliable methods for unveiling multifractal properties, specially when real-world time series are under analysis. However, little is known about how several aspects, like artefacts during the data acquisition process, affect its results. In this work we have numerically investigated the performance of Multifractal Detrended Fluctuation Analysis applied to synthetic finite uncorrelated data following a power-law distribution in the presence of additive noise, and periodic and randomly-placed outliers. We have found that, on one hand, spurious multifractality is observed as a result of data finiteness, while additive noise leads to an underestimation of the exponents hq for q<0 even for low noise levels. On the other hand, additive periodic and randomly-located outliers result in a corrupted inverse multifractality around q=0. Moreover, the presence of randomly-placed outliers corrupts the entire multifractal spectrum, in a way proportional to their density. As an application, the multifractal properties of the time intervals between successive aircraft landings at three major European airports are investigated.