Air Traffic Control Research Articles

Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments

Air Traffic Controllers’ (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky.

Automatic recognition of airliners wake turbulence using various techniques of machine intelligence

Airplanes generate severe turbulent airflow, known as trailing vortices, which can have an adverse control impact on following airplanes. Aviation authorities worldwide impose separation standards between leading and following airplanes to reduce the capacity of airports to handle large volumes of traffic. Optimizing these separations is highly sought after. These separations are optimized based on several factors such as airplane size, weight, and design in addition to weather conditions. The recognition of trailing vortices is usually done manually through examination of pilot reports and analysis of flight data recorders (FDRs). This process can be subjective and depends greatly on the analyst's experience. Automated processes based on Artificial Intelligence (AI), such as Machine Learning (ML) and Computational Intelligence (CI), have shown a potential to better deal with such problems. Results obtained from these various techniques are presented to show their degree of appropriateness. Flight event identification, such as encounters with airplane vortices, wind shear, turbulence, and uncommanded control, can help establish aircraft warning and monitoring systems to enhance automatic flying, especially during landing and take-off. The developed model can attain great assurance recognition of the aircraft wake turbulence by supporting the air traffic controllers with the decision information in actual work to ensure aircraft safety. The success rates obtained from the investigated CI models compare favorably well to results obtained by either manual and/or other CI-based techniques when applied to vortex identification.

Air Traffic Controller Workload Detection Based on EEG Signals.

The assessment of the cognitive workload experienced by air traffic controllers is a complex and prominent issue in the research community. This study introduces new indicators related to gamma waves to detect controllers' workload and develops experimental protocols to capture their EEG data and NASA-TXL data. Then, statistical tests, including the Shapiro-Wilk test and ANOVA, were used to verify whether there was a significant difference between the workload data of the controllers in different scenarios. Furthermore, the Support Vector Machine (SVM) classifier was employed to assess the detection accuracy of these indicators across four categorizations. According to the outcomes, hypotheses suggesting a strong correlation between gamma waves and an air traffic controller's workload were put forward and subsequently verified; meanwhile, compared with traditional indicators, the indicators associated with gamma waves proposed in this paper have higher accuracy. In addition, to explore the applicability of the indicator, sensitive channels were selected based on the mRMR algorithm for the indicator with the highest accuracy, β + θ + α + γ, showcasing a recognition rate of a single channel exceeding 95% of the full channel, which meets the requirements of convenience and accuracy in practical applications. In conclusion, this study demonstrates that utilizing EEG gamma wave-associated indicators can offer valuable insights into analyzing workload levels among air traffic controllers.

Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels.

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process.

Simulation analysis of BOP thermal management system for hydrogen fuel cell bus

Hydrogen fuel cell bus has been rapidly developed. As an important part of the hydrogen fuel cell system, its BOP (Balance of plant) mainly includes air compressor, controller, and intercooler. In practical applications, the BOP thermal management system generally includes separated cooling circuits. This study proposes an integrated BOP thermal management system, in which the air compressor, the controller and the intercooler share the same cooling circuit. A 1-D numerical model is built in this paper. On the basis of the model, the 13 possible layout schemes of cooling circuit are investigated. Additionally, the BOP temperature variation characteristics under the UDDS (Urban Dynamometer Driving Schedule) condition are also analyzed. The results indicate that not all layouts of integrated scheme can meet the cooling requirements. To meet cooling requirements of the whole BOP, the coolant should pass through the intercooler first in the serial schemes. As for the parallel schemes, the air compressor should be in a single branch, and the controller and the intercooler should be in series successively at another branch. Under the same total flow rate, the average temperature of BOP in the series scheme can be lower than that in parallel scheme by 8 %. In the three components, the intercooler is most affected by the stack power. This study provides reference for the design of the BOP thermal management system.

Global information aware network with global interaction graph attention for infrared small target detection

AbstractDetecting small targets in infrared images is crucial for ground surveillance and air traffic control. However, distinguishing small infrared targets from similar backgrounds is challenging due to their lack of structural and textural characteristics. To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. The GIGA consists of a global interaction layer (GILayer), graph attention weights (GAW), and a global relational learning (GRL) module. Specifically, the GILayer dynamically learns global inter‐pixel relationships of small target images by enhancing the dependencies between feature dimensions. The GAW component calculates pixel‐by‐pixel similarity across the entire feature map using graph attention mechanisms, while the GRL module retains critical similarity features in the feature extraction network, thereby facilitating small target detection. Additionally, the multi‐scale context fusion module utilises self‐attention and dilation convolution to complement richer feature details at different scales. Experimental results on both natural and synthetic datasets demonstrate the proposed method's superiority over other state‐of‐the‐art conventional and deep learning approaches in infrared small target detection.

Building interfaces between unmanned aircraft systems (UAS), air traffic controllers (ATCo), and the national airspace system (NAS): A software training platform

Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.

Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in five levels. Time Frame Return Frequency TextRank text classification method based on key content extraction and text classification model based on Convolutional Neural Network and Bidirectional Encoder Representations from Transforms models were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

Development of Interactive Learning Media Based on the Aleis (ATC Licensce Exam Information System) Website for Air Traffic Controllers at Makassar Aviation Polytechnic

Development of Interactive Learning Media Based on the Aleis (ATC Licensce Exam Information System) Website for Air Traffic Controllers at Makassar Aviation Polytechnic

Influence of gestational window on offspring vascular health in rodents with in utero exposure to electronic cigarettes.

Studies have shown cerebrovascular dysfunction in offspring with full-gestational electronic cigarette (Ecig) exposure, but little is known about how individual trimester exposure impacts offspring health. This study aimed to determine if there is a critical window during gestation that contributes to vascular and anxiety-like behavioural changes seen with full-term exposure. To test this, rats were time-mated, and the pregnant dams were randomly assigned to Ecig exposure during first trimester (gestational day, GD2-7), second trimester (GD8-14), third trimester (GD15-21) or full-term gestation (GD2-21). We also assessed the effect of maternal preconception exposure. Both male and female offspring from all maternal exposure conditions were compared to offspring from dams under ambient air (control) conditions. Ecig exposure consisted of 60-puffs/day (5days/week) using either 5 or 30watts for each respective exposure group. We found that maternal exposure to Ecig in the second and third trimesters resulted in a decrease (23-38%) in vascular reactivity of the middle cerebral artery (MCA) reactivity in 3- and 6-month-old offspring compared to Air offspring. Further, the severity of impairment was comparable to the full-term exposure (31-46%). Offspring also displayed changes in body composition, body mass, anxiety-like behaviour and locomotor activity, indicating that Ecigs influence neurodevelopment and metabolism. Maternal preconception exposure showed no impact on offspring body mass, anxiety-like behaviour, or vascular function. Thus, the critical exposure window where Ecig affects vascular development in offspring occurs during mid- to late-gestation in pregnancy, and both 5W and 30W exposure produce significant vascular dysfunction compared to Air. KEY POINTS: Exposure to electronic cigarettes (Ecigs) is known to increase risk factors for cardiovascular disease in both animals and humans. Maternal Ecig use during pregnancy in rodents is found to impair the vascular health of adolescent and adult offspring, but the critical gestation window for Ecig-induced vascular impairment is not known. This study demonstrates Ecig exposure during mid- and late-gestation (i.e. second or third trimester) results in impaired endothelial cell-mediated dilatation (i.e. middle cerebral artery reactivity) and alters anxiety-like behaviour in offspring. Maternal exposure prior to conception did not impact offspring's vascular or anxiety-like behavioural outcomes. Rodent models have been a reliable and useful predictor of inhalation-induced harm to humans. These data indicate maternal use of Ecigs during pregnancy should not be considered safe, and begin to inform clinicians and women about potential long-term harm to their offspring.

Robust differential gene expression patterns in the prefrontal cortex of male mice exposed to an occupationally relevant dose of laboratory-generated wildfire smoke.

Wildfires have become common global phenomena concurrent with warmer and drier climates and are now major contributors to ambient air pollution worldwide. Exposure to wildfire smoke has been classically associated with adverse cardiopulmonary health outcomes, especially in vulnerable populations. Recent work has expanded our understanding of wildfire smoke toxicology to include effects on the central nervous system and reproductive function; however, the neurotoxic profile of this toxicant remains ill-explored in an occupational context. Here, we sought to address this by using RNA sequencing to examine transcriptomic signatures in the prefrontal cortex of male mice modeling career wildland firefighter smoke exposure. We report robust changes in gene expression profiles between smoke-exposed samples and filtered air controls, evidenced by 2,862 differentially expressed genes (51.2% increased). We further characterized the functional relevance of these genes highlighting enriched pathways related to synaptic transmission, neuroplasticity, blood-brain barrier integrity, and neurotransmitter metabolism. Additionally, we identified possible contributors to these alterations through protein-protein interaction network mapping, which revealed a central node at ß-catenin and secondary hubs centered around mitochondrial oxidases, the Wnt signaling pathway, and gene expression machinery. The data reported here will serve as the foundation for future experiments aiming to characterize the phenotypic effects and mechanistic underpinnings of occupational wildfire smoke neurotoxicology.

Impacts of COVID-19 on Air Traffic Control and Air Traffic Management: A Review

The global air transportation system continues to be greatly impacted by operational changes induced by the COVID-19 pandemic. As air traffic management (ATM) focuses on balancing system capacity with demand, many facets of ATM and system operations more broadly were subjected to dramatic changes that deviate from pre-pandemic procedures. Since the start of the COVID-19 pandemic when air travel became one of the first transport modes to be impacted by lockdown procedures and travel restrictions, a geographically diverse cohort of researchers began investigating the impacts of the COVID-19 pandemic on air navigation service providers, airline and airport operations, on-time performance, as well as airline network structure, connectivity, crew scheduling, and service impacts due to pilot and crew shortages. In this study, we provide a comprehensive review of this aforementioned body of research literature published during one of the most tumultuous times in the history of aviation, specifically as it relates to air traffic management and air traffic control. We first organize the reviewed literature into three broad categories: strategic air traffic management and response, air traffic control and airport operational changes, and air traffic system resilience. Then, we highlight the main takeaways from each category. We emphasize specific findings that describe how various aspects of the air transportation systems could be improved in the domestic and global airline industry post-COVID. Lastly, we identify specific changes in operational procedures due to the COVID-19 pandemic and suggest future industry trends as informed by the literature. We anticipate this review article to be of interest to a broad swath of aviation industry and intercity transportation audiences.

Perancangan Aplikasi Kamus Air Traffic Control Berbasis Mobile Android

This study discusses the Air Traffic Controller dictionary where this dictionary will discuss all the initials that have developed in the world of air police, which so far all information about the rules in regulating flights is only in written form (Manual Dictionary). The purpose of this study is to help air transportation speed directors recall some of the existing definitions in the world of aviation, so that it is hoped that there will be no more misunderstandings between pilots and air transportation speed controllers. The method used by the author is the waterfall method, where the flow is Analysis, Design, Implementation and testing. The Unified Modeling Language design will help readers understand this research. The results of this study illustrate that the application can run on an Android mobile device.

Mechanical behavior of glass fiber-reinforced polyester in a humid environment

Glass fiber composite materials have been well known in certain maritime applications for a long time, being used in several applications in different fields. The characteristics of Glass fiber-reinforced Polyester (GFRP) depend on the contained fibers, the matrix used, and the fiber-to-total volume ratio. GFRP is a polymer material used in various fields; it is generally preferred for its ease of installation and its very long lifespan, even in the presence of aggressive fluids. The aim of this research is to study the evolution of mechanical properties (tensile strength and flexural strength) of GFRP preserved in different environments such as open air (control), drinking water, and seawater (harsh environment). Additionally, tests on GFRP are conducted through chemical characterization studies or SEM observation of the resin. Lastly, GFRP is considered to be chemically stable for a conservation period of one year in seawater. Finally, despite the conservation of GFRP for a year in a humid and sulphate environment, we note that the connection between the fibers and the matrix is ​​in good condition, or the conservation environment has no influence on the GFRP composite.

Defining and characterizing the operational context for human systems integration

AbstractMany efforts from multiple academic and industrial disciplines have studied the notion of context. The systems engineering and human systems integration fields, however, lack a generalized definition and characterization of context, in particular the operational context of complex sociotechnical systems. This paper reviews context definitions and builds a generalized definition of the operational context of complex sociotechnical systems. The objective is then to identify and extract context properties which are often implicit in the literature and do not necessarily appear in the definitions. However, eliciting them is helpful for understanding what context is made of, how it relates to complex sociotechnical systems comprising human and non‐human agents, and how an early understanding of the operational context can prove valuable to designing efficient and robust systems. Our analysis is followed by an example of how our definition and derived properties apply to the case study of the design of a remote and virtual air traffic control center.

Optimizing virtual energy sharing in renewable energy communities of residential users for incentives maximization

Renewable energy communities (RECs) are considered a promising tool for putting the citizens at the center of the energy transition, while also promoting self-sufficiency coming from local resources and decarbonization through high penetration of renewables. A key challenge when operating RECs is represented by the number of decision variables to consider depending on the number and type of community participants and distributed technologies, while also considering the associated uncertainties. Moreover, the monetarization of energy shared in the community for benefitting residential users is crucial. The contribution of this paper is to present an innovative stochastic linear programming model for optimizing the energy sharing in RECs to maximize revenues associated with the incentives for the energy shared as established by the Italian regulation. The REC under study consists of a condominium with a PV plant installed on the rooftop, and air conditioning and battery storage systems installed in each apartment. The problem is to find the optimal control strategies for air conditioning systems and batteries with a 15-minute time-step, which maximize the expected revenue from energy sharing while meeting the users’ comfort requirements and preventing users’ bills from increasing. Numerical results demonstrate the effectiveness of the optimization model to maximize the energy shared and the related revenues through the optimal control of installed assets. The combined optimized strategies of both air conditioning and batteries allow for finding the best performance of the REC in terms of maximization of the energy shared. In this latter case, the expected total revenue for users for the energy sharing increases by 59.7 %, 38.7 % and 12.6 % as compared to the baseline case with no optimal control, the case with control of air conditioning only, and the case with control of batteries only, respectively.

A fusion framework to characterize and evaluate air traffic clusters based on potential field theory

The dramatic increase in flight volume and density has inevitably led to the small-scale clustering of aircraft, presenting significant challenges for air traffic controllers due to the complex interrelationships involved. In order to better characterize the clustering phenomenon and its complexity with massive real-time data, this paper introduces a data-driven fusion framework. This framework incorporates an interdependent network and a novel indicator system based on potential field theory to characterize the interaction and superposition effects within these clusters, encompassing aircraft, airspace waypoints, and air traffic controllers. To further explore the complexity of clusters according to the characteristic indicator system, a deep learning model is employed to identify these cluster scenarios. Training results on these scenarios indicate that the deep learning model, using the GRU-attention model, excels in capturing the complex features of clusters. In practical scenario experiments, the proposed fusion framework integrates the interdependent network, characteristic indicator system, and the GRU-attention model. It has proven effective in characterizing clusters and evaluating their complexities. This not only provides real-time alerts but also supports decision-making for air traffic controllers in challenging situations.

Perinatal nicotine vaping exposure induces pro-myofibroblastic phenotype in rat bone marrow-derived mesenchymal stem cells

Perinatal nicotine exposure via tobacco smoking results in increased proclivity to chronic lung disease (CLD); however, the underlying molecular mechanisms remain incompletely understood. We previously demonstrated that in addition to nicotine’s direct effects on the developing lung, there are also adverse molecular alterations in bone marrow-derived mesenchymal stem cells (BMSCs), which are vital to lung injury repair. Whether perinatal nicotine exposure via electronic-cigarette (e-cig) vaping also adversely affects BMSCs is unknown. This is highly relevant due to marked increase in e-cig vaping including by pregnant women. Hypothesizing that perinatal nicotine exposure via e-cig vaping predisposes BMSCs to a pro-myofibroblastic phenotype, pregnant rat dams were exposed to fresh air (control), vehicle (e-cig without nicotine), or e-cig (e-cig with nicotine) daily during pregnancy and lactation. At postnatal day 21, offspring BMSCs were isolated and studied for cell proliferation, migration, wound healing response, and expression of key Wnt and PPARγ signaling intermediates (β-catenin, LEF-1, PPARγ, ADRP and C/EBPα) and myogenic markers (fibronectin, αSMA, calponin) proteins using immunoblotting. Compared to controls, perinatal e-cig exposure resulted in significant decrease in BMSC proliferation, migration, and wound healing response. The expression of key Wnt signaling intermediates (β-catenin, LEF-1) and myogenic markers (fibronectin, αSMA, calponin) increased significantly, while PPARγ signaling intermediates (PPARγ, ADRP, and C/EBPα) decreased significantly. Based on these data, we conclude that perinatally e-cig exposed BMSCs demonstrate pro-myofibroblastic phenotype and impaired injury-repair potential, indicating a potentially similar susceptibility to CLD following perinatal nicotine exposure via vaping as seen following parenteral perinatal nicotine exposure.

Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.

Role of Law in Combatting Air Pollution

Air pollution in urban areas arises from multiple sources, which may vary with location to location and activity to activity of development. Anthropogenic activities such as rampant industrialization, exploitation and over consumption of natural resources, population explosion are major contributors of air pollution. Regulations and legislations adopted are enumerated. The Clean Air Act envisaged guidelines for industries regarding emission. It has been seen and observed that in spite several rules and regulations, not much has been achieved in controlling air pollution particularly in countries like India. India needs to make tough legislations The present paper is an effort to discuss various aspects of air pollution and control legislation in India emphasizing on the history, present scenario, gaps and drawbacks. It also presents legislative controls with judicial response to certain landmark judgments related to air pollution. The down sides related to enforcement mechanism for the effective implementation of environmental laws for air pollution control have been highlighted.