International Airport Research Articles

Deep Learning Enhanced Wind Speed and Direction Forecasting for Airport Regions

Abstract This work introduces a deep-learning architecture tailored for accurate wind speed and direction forecasting for airports using a grid-based input. Moving beyond the limitations of conventional forecasting methods, which struggle with rapid and localized atmospheric changes and demand substantial computational power, this study positions a machine-learning approach as a superior alternative for wind nowcasting. By employing a comprehensive dataset covering 75 years, the proposed model distinguishes itself by achieving a mean absolute error of 1.26 m/s for wind speed and 16.18° for wind direction in the examined location, Madeira International Airport area. The model's robustness was further validated using transfer learning across ten global airport areas, each with diverse atmospheric conditions, resulting in consistent accuracy. Additionally, a precision forecast focused specifically on the runway was conducted, and its performance was found to be consistent with the broader spatial area forecasts. These results underscore the potential of machine learning in wind prediction for aviation, highlighting a promising pathway for future advancements in weather forecasting technology.

Equatorial plasma bubble model and integrity risk evaluation for ground based augmentation system in Hong Kong

The spatial gradient induced by the Equatorial Plasma Bubble (EPB) in low latitude regions is a major challenge for the Ground Based Augmentation System (GBAS). To facilitate the implementation and operation of GBAS Approach Service Type (GAST)-D at Hong Kong International Airport, the impact of EPB induced spatial gradients needs to be analyzed. Previous simulations using a two-dimensional trapezoid model neglected the three-dimensional structure of the EPB, assuming ionospheric delay on a thin shell at a specific altitude. To address this limitation, this paper adopts a cube above the magnetic equator to characterize the EPB threat model. The ionospheric delay difference between satellite signals passing through the EPB model is limited by the upper bound spatial gradient derived with the data collected from Hong Kong Satellite Positioning Reference Station Network. The simulation results reveal that ionospheric monitors in GAST D can satisfy the Category II/III approach requirement, i.e., the probability of missed detection PMD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${P}_{\ ext{MD}}$$\\end{document} for the differential range error Er larger than 2.75 m is lower than 1×10-9\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1\ imes 10}^{-9}$$\\end{document}. The potentially hazardous event with the largest Er of 2.47 m and PMD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${P}_{\ ext{MD}}$$\\end{document} of 1.86×10-9\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1.86\ imes 10}^{-9}$$\\end{document} occurs when the satellite signal moves parallel to the equator near the Equatorial Ionization Anomaly (EIA) region, and its significant portion traverses in the EPB depletion region.

ADS-B Communication Security: Assessing the Performance of Format-Preserving Lightweight Block Ciphers

In this work, we document an investigation regarding the vulnerabilities of ADS-B (automatic dependent surveillance-broadcast) communication, considering the risks to operational security. The ADS-B technology is a communication protocol to transmit surveillance information, that is, data related to position, altimetry, and speed, from the aircraft's avionics, an evolution to radars. We present an encryption solution for this communication using format-preserving encryption implemented in a microcontroller-embedded system. We also evaluate the use of lightweight symmetric block ciphers for better computational performance. When used as a pseudorandom function, we observe that such ciphers maintain a high entropy output value with low computational cost --- up to sixteen times faster for similar entropy values. Finally, the computational performance obtained with the proposed solution is analyzed by processing real-time data from aircraft in the landing and takeoff phase at the international airport of Recife/Guararapes - Gilberto Freyre, based in Recife/PE, Brazil.

Performance improvement of the RSM970S radar speed controller of ASECNA, Douala International Airport

Performance improvement of the RSM970S radar speed controller of ASECNA, Douala International Airport

Can rail reduce British aviation emissions?

We analyse UK airport origin–destination data from 1990 to 2021 to understand the extent to which a modal shift to rail may reduce aviation emissions. We find that 41 % of UK aviation passengers travel on routes that can be done by rail in less than 24 h. However, these passengers account for only 14 % of UK aviation emissions because long-haul flights contribute the majority of emissions. Some popular destinations (e.g. Spanish Islands) are inaccessible by rail and may be suitable for destination switching. We also find rapid growth in flights to international hub airports used for connecting journeys. This has implications for carbon accounting, suggesting that a significant and growing proportion of UK residents’ aviation emissions are being excluded from national accounts. Finally, the paper recommends some interventions that might encourage a modal shift to rail.

Electric airport ferry vehicle scheduling problem for sustainable operation

This study discusses the challenges and solutions surrounding the scheduling of electric airport ferry vehicles (EAFVs) in the aviation industry. With the rise of electric vehicle technology and the push for sustainability, the adoption of EAFVs presents a significant opportunity for reducing emissions in airport ground transportation. However, scheduling EAFVs involves integrated considerations of operational scheduling and charging scheduling. To address these challenges, this study proposes a mixed-integer nonlinear programming (MINLP) model aimed at minimizing total costs associated with EAFV utilization while adhering to flight time window constraints and ensuring electric power availability. Furthermore, we transform the proposed MINLP model into a solvable mixed-integer linear programming model, facilitating its solution using off-the-shelf commercial solvers. To meet the requirements of the large international airport, we propose a stabilization approach for solving the large-scale instances. Finally, we further analyze the influence of crucial parameters, including the number of EAFVs deployed, the number of flights for servicing, and the charging power of charging facilities. Managerial policies are proposed for the airport managers based on the findings. This research addresses both theoretical and practical aspects, offering valuable policy recommendations for the effective EAFV management and providing a foundation for further exploration in EAFV scheduling. The findings contribute to the advancement of sustainable transportation practices in the aviation industry.

Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection

Windshear is a microscale meteorological phenomenon that can be dangerous to aircraft during the take-off and landing phases. Accurate windshear detection plays a significant role in air traffic control. In this paper, we aim to investigate a machine learning method for windshear detection based on previously collected wind velocity data and windshear records. Generally, the occurrence of windshear events are reported by pilots. However, due to the discontinuity of flight schedules, there are presumably many unreported windshear events when there is no flight, making it difficult to ensure that all the unreported events are all non-windshear events. Hence, one of the key issues for machine-learning-based windshear detection is determining how to correctly distinguish windshear cases from the unreported events. To address this issue, we propose to use a positive and unlabeled learning method in this paper to identify windshear events from unreported cases based on wind velocity data collected by Doppler light detection and ranging (LiDAR) plan position indicator (PPI) scans. An optimal-transport-based optimization model is proposed to distinguish whether a windshear event appears in a sample constructed by several LiDAR PPI scans. Then, a binary classifier is trained to determine whether a sample represents windshear. Numerical experiments based on the observational wind velocity data collected at the Hong Kong International Airport show that the proposed scheme can properly recognize potential windshear cases (windshear cases without pilot reports) and greatly improve windshear detection and prediction accuracy.

Examining airport agility at air cargo hub airports

The Covid-19 pandemic impacted nearly every aspect of airport operations, highlighting the importance of airport agility. Due to its importance, it is crutial for us to understand the role of agility at airports prior to and during unexpected disruptions. We investigated airport agility for air cargo hub airports using a structural equation model, particularly at Incheon International Airport (ICN). This study uses 27 variables to evaluate the agility of the air cargo business for a hub airport. We find that digital transformation, flight services, information sharing, and accessibility enhance the agility of air cargo hub airports. The survey shows that air cargo experts perceive ICN airport as having satisfactory agile response capabilities. Agility is essential to resilience, which will deliver a competitive advantage and aid the long-term prosperity of the air cargo business. Airports must collaborate to share, align, and improve their collective resilience to improve the productivity and performance of air cargo hubs. Developing innovative airport models enabled by new technologies, such as digital transformation, increases the efficiency and agility of the airport. The ICN's strategy as an air cargo hub airport is responsive to customer needs to mitigate disruptions through digitalization, offering value-added services with adaptability. The application of digitalization improves the airport's agility and efficiency and mitigates uncertainty in the air cargo industry.

Research on Check-In Baggage Flow Prediction for Airport Departure Passengers Based on Improved PSO-BP Neural Network Combination Model

Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers.

Study on Organizational Model of Apron Operation in Airport

With the rapid development of China's economy, China has achieved a breakthrough in air transport, traffic and transport capacity of large airports has increased significantly, many large airports present a trend of large-volume, high-density transport. Based on the analysis of ramp capacity and ramp efficiency after the handover, the proper use of independent ramp control can improve the operational efficiency of the airport, increase the normal rate of flight clearance, and reduce the workload of the air traffic control tower, to improve the safety of aircraft on the Apron. By learning the advanced experience of foreign countries, the organization mode of large-scale Airport Apron operation is calculated, and the airport apron operation capacity and efficiency are analyzed. The resulting data is weighted by Analytic hierarchy process. Finally, the feasibility of the result is verified by the case of Chengdu Shuangliu International Airport.

Incorporation of Q method into DEA cross-efficiency: A case study on airport assessment

Though widely used, traditional Data Envelopment Analysis (DEA) methods have limitations when it comes to accurately ranking airport performance. The Cross-Efficiency Method (CEM), derived from DEA, addresses these limitations. However, recently, the conventional CEM has been considered for further improvements. Firstly, it assumes equal significance for all Decision Makers’ (DMs) viewpoints by using the arithmetic mean for overall cross-efficiency calculation, which is unrealistic. Secondly, it does not consider DMs’ viewpoints psychologically. Thirdly, it does not often consider achieving consensus through efficiency calculations. Finally, it includes all viewpoints for efficiency calculation, regardless of their relevance, which can bias the results. Thus, the primary objective of this study is to develop hybrid Q-based CEMs to address these shortcomings. Also, this study is the first to use the Q methodology to address the limitations of traditional CEM. The Q-based aggressive and benevolent CEMs, known as QACEM and QBCEM, provide policymakers with several advantages. Firstly, they allow for the exclusion of irrelevant viewpoints from the analysis. Secondly, they enable the calculation of each DM’s appropriate contribution. Thirdly, they capture DMs’ psychological preferences using the Q methodology. Lastly, they facilitate consensus-building by extracting group perspectives through factor analysis. The Q-based CEMs were utilized to assess the performance of 25 Iranian international airports and demonstrated their effectiveness. Selecting the optimal loading factors of 0.6 and 0.7, QACEM and QBCEM included the highest possible number of DM’s viewpoints, which were 24 and 25 respectively. The results indicate that airports BND, AWZ, and OMH demonstrated satisfactory performance.

In Case You Haven't Heard…

An airport in Oregon is trying to ease nervous flyers' concerns by providing them with access to an unusual animal therapy program, Business Insider reported Nov. 4. Portland International Airport says it has partnered with Mountain Peaks Therapy to bring llamas and alpacas into its terminals every few weeks, hoping to alleviate some of the stress associated with air travel, according to NPR. The farm, based in Ridgefield, Washington, has five llamas and six alpacas, which it says are certified to interact with passengers in a calming manner. Its website says it costs about $650 an hour per animal. Studies have shown that animal therapy has minor to moderately positive effects on those struggling with an array of mental illnesses, though academics say more research needs to be done on its efficacy. According to UCLA Health, animal therapy can promote the release of mood‐boosting hormones, lower anxiety, and provide a “happy distraction.” Most existing studies are focused on therapy dogs, which are increasingly being used in airports, such as London Stansted and Istanbul Airport.

Investigating the Effect Of Communication and Teamwork on Employee Performance: An Empirical Study in Indonesia Airport Company

<span lang="EN-US">The study aimed to investigate the effect of communication and teamwork on employee performance. The type of research used is a quantitative method, Primary data sources were obtained through a survey of all employees in the Operations and Services section of PT. Angkasa Pura 1, </span><span>General </span><span lang="EN-US">Ahmad Yani International Airport Branch Office, Semarang</span><span>, Central Java, Indonesia</span><span lang="EN-US">. The technique used </span><span>a</span><span lang="EN-US"> random sampling so a total of 52 employees were obtained. The results showed that communication had a positive effect on employee performance. Other finding concluded that teamwork had a positive and significant effect on employee performance. The results contribute to providing important evidence </span><span>of </span><span lang="EN-US">building cooperation and communication in organizations.</span>

Detection and genetic characterization of multidrug-resistant staphylococci isolated from public areas in an international airport

The environmental realm has been acknowledged as a pivotal arena for the emergence and propagation of antimicrobial resistance. To further explore insight into antimicrobial resistance dynamics beyond clinical and veterinary settings, we embarked on an environmental surveillance initiative targeting the prevalence of antibiotic-resistant bacteria within the bustling confines of an international airport in Japan. Our findings illuminate a high prevalence of methicillin-resistant staphylococci (46.3%) on frequently contacted surfaces in the public domain. Notably, Staphylococcus haemolyticus and S. epidermidis emerged as the preeminent carriers of the mecA gene. Intriguingly, we encountered a virulent strain of livestock-associated MRSA harboring a PVL-positive ST1232 clone, CC398 lineage. Further scrutiny unveiled a repertoire of resistance mechanisms, the methicillin-resistant isolates exhibited two or more resistance genes conferring resistance against different types of antibiotics, including beta-lactams, macrolides, lincosamides, aminoglycosides, chloramphenicol, and fosfomycin. Revealing multidrug-resistant CoNS and a LA-MRSA across various surfaces in urban public areas unearths a looming public health hazard. Thus, implementation of molecular surveillance is imperative, augmenting our capacity for early detection and mitigation of the insidious spread and potential transfer of antibiotic resistance genes and virulence factors amidst urban settings, notably within pivotal nodes such as airports.

Analyzing Passenger Flows in an Airport Terminal: A Discrete Simulation Model

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario.

Screening for Asylum at Airports: The Implementation of the ‘Manifestly Unfounded’ Filter in France and Germany, 1980–2005

ABSTRACTThis historical paper examines pre‐screening procedures for asylum‐seekers at Western European international airports during the period from the 1980s to the 2000s, with a particular focus on France and Germany as case studies. It highlights the tension between attempts to standardise the application of the ‘manifest unfoundedness’ criterion for asylum claims at the border through intergovernmental harmonisation efforts and the significant variations observed in how this criterion was implemented both between and within Member States of the European Community and later the European Union. By exploring these discrepancies, the paper emphasises the context‐dependent nature of enforcing asylum rules and the influence of local dynamics on the effectiveness of these norms. It begins by providing background on the refugee landscape in the 1980s and the prevalent desire among Western European policy‐makers to reduce the number of asylum‐seekers at the borders. The paper then discusses efforts to harmonise procedures and the adoption of a common, and soon distorted, yardstick pertaining to the ‘manifest unfoundedness’ of asylum claims. Furthermore, the article examines disparities in implementation across countries and within them, shedding light on differences in norms, procedures and institutional dynamics that led to divergent approaches at the local level.

Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.

Life Cycle Prediction of Airport Operation Based on System Dynamics

Traditional airport development planning often overlooks an in-depth consideration of the airport operation life cycle, which frequently causes deviations from planned objectives during operation. This paper presents a framework for predicting and segmenting the airport operation life cycle by integrating the dynamic characteristics of the System Dynamics (SD) model with the static properties of Logistic modeling to examine the development trajectory of airport operations. The influencing factors in this model are selected across three levels: airport, city, and macro-environment. A system dynamics model of airport operation is constructed using causal loop diagrams and system flow diagrams. Using Guangzhou Baiyun International Airport (CAN) as a case study, the airport’s operational capacity from 2005 to 2035 is predicted through SD simulation. Subsequently, the airport operation life cycle from 2005 to 2050 is forecasted and segmented using Logistic modeling. The results indicate that, under the standard scenario, CAN’s operational capacity experiences two declines in 2016 and 2020, attributed to airport construction and emergencies. Logistic modeling identifies three distinct life cycle phases in the airport’s operation. Furthermore, by comparing various airport operation scenarios, the analysis reveals that fluctuations in the city economy significantly impact the airport’s operational system without altering its overall development trajectory. In contrast, the occurrence of emergencies can substantially modify the airport operation life cycle.

Genomic surveillance of Canadian airport wastewater samples allows early detection of emerging SARS-CoV-2 lineages

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has shown wastewater (WW) surveillance to be an effective means of tracking the emergence of viral lineages which arrive by many routes of transmission including via transportation hubs. In the Canadian province of Ontario, numerous municipal wastewater treatment plants (WWTPs) participate in WW surveillance of infectious disease targets such as SARS-CoV-2 by qPCR and whole genome sequencing (WGS). The Greater Toronto Airports Authority (GTAA), operator of Toronto Pearson International Airport (Toronto Pearson), has been participating in WW surveillance since January 2022. As a major international airport in Canada and the largest national hub, this airport is an ideal location for tracking globally emerging SARS-CoV-2 variants of concern (VOCs). In this study, WW collected from Toronto Pearson’s two terminals and pooled aircraft sewage was processed for WGS using a tiled-amplicon approach targeting the SARS-CoV-2 virus genome. Data generated was analyzed to monitor trends of SARS-CoV-2 lineage frequencies. Initial detections of emerging lineages were compared between Toronto Pearson WW samples, municipal WW samples collected from the surrounding regions, and Ontario clinical data as published by Public Health Ontario. Results enabled the early detection of VOCs and individual mutations emerging in Ontario. On average, the emergence of novel lineages at the airport preceded clinical detections by 1–4 weeks, and up to 16 weeks in one case. This project illustrates the efficacy of WW surveillance at transitory transportation hubs and sets an example that could be applied to other viruses as part of a pandemic preparedness strategy and to provide monitoring on a mass scale.

Zwitterionic, cationic, and anionic PFAS in freshwater sediments from AFFF-impacted and non-impacted sites of Eastern Canada

Zwitterionic, cationic, and anionic per- and polyfluoroalkyl substances (PFAS) were investigated in freshwater sediments of Canada, including sites impacted by aqueous film-forming foams (AFFFs). The first step of the project involved optimizing the extraction method with equilibrated sediment-water-AFFF samples. The analytical method had acceptable linearity, accuracy, and precision in the sediment matrix, and was further validated with NIST SRM 1936. In the second step of the project, the method was applied to determine over 70 target PFAS in field-collected sediments (n = 102). At federal contaminated sites of Ontario, Newfoundland, and Québec (ditches and creeks at international airports with fire training or fire equipment testing areas), summed PFAS averaged 30ng/g (max of 160ng/g) with molecular patterns dominated by perfluorooctane sulfonate (maximum PFOS: 84ng/g). Based on maximum observed concentrations >10ng/g, other key PFAS at these AFFF-impacted sites included negative ion mode perfluorohexane sulfonate, perfluorohexane sulfonamide, fluorotelomer sulfonates (6:2 FTS and 8:2 FTS) and 5:3 fluorotelomer acid, and positive ion mode N-dimethylammoniopropyl perfluorohexane sulfonamide and 5:1:2 fluorotelomer betaine. In contrast, environmental sediment samples collected at a larger spatial scale (province-wide survey) were characterized by low ΣPFAS (generally <1ng/g), with PFOS/PFOA below chronic toxicity thresholds for aquatic life.