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![Structure of a long short-term memory cell [12].](publication/362450955/figure/fig1/AS:11431281085775775@1663880665649/Structure-of-a-long-short-term-memory-cell-12.png)
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![Figure 5. Structure of a long short-term memory cell [12].](publication/362450955/figure/fig1/AS:11431281085775775@1663880665649/Structure-of-a-long-short-term-memory-cell-12_Q320.jpg)
Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deteriorat...
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... is called gradient clipping. To encounter the vanishing gradient problem, LSTM cells (see Figure 5) in the hidden layer use the so-called gating mechanism. ...
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... is called gradient clipping. To encounter the vanishing gradient problem, LSTM cells (see Figure 5) in the hidden layer use the so-called gating mechanism. In the gating mechanism, the cell stores values over arbitrary time intervals, and the gates noted as Γ (see Equation (15) [12]) regulate the flow of information in and out of a cell. ...
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The study of human factors in aviation makes an important contribution to safety. Within this discipline, real-time simulations (RTS) are a very powerful tool. The use of simulators allows for exercises with controlled air traffic control (ATC) events to be designed so that their influence on the performance of air traffic controllers (ATCOs) can b...
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... Conflicts A r t i c l e i n P r e s s can be predicted and resolved using this system, powered by LSTM networks, and enhances situational awareness. These advancements demonstrate the potential of AI to improve air traffic management and support decision-making [11], [12]. ...
... It emphasizes the necessity of explainable AI (XAI) to increase trust in human operators and transparency [6]. AI algorithms to forecast air traffic, weather conditions, flight delays, and conflict resolution are just a few examples of the AI-based strategies discussed in the following paragraphs [11], [12]. In addition, the research investigates how strategic conformal automation, cooperative automation, and machine learning can improve system performance and reduce air traffic controller workload [18], [19]. ...
... The increasing spread of the COVID-19 virus has caused the aviation sector to be most significantly impacted by travel restrictions, requiring process improvements in flight scheduling management [1,2]. One way to increase the efficiency of flight schedule management processing is to use mathematical model optimization [3,4]. ...
... The results of this research are in the form of a mathematical model that can be a solution in air traffic management. The resulting model is a mathematical formula to determine the function that can minimize the costs contained in equation (2). Then produce a formulation to determine capacity limits in equations (3)-(6), produce a formula to determine capacity limits, which produces mathematical formulations in equations (7)-(9) and equation (10) and produce a formulation to determine the variable domain contained in equation (11). ...
In this paper, the object of the study is determining air traffic scheduling management by optimizing mathematical models. The problem in this study is that the COVID-19 pandemic has significantly disrupted air traffic, resulting in changes to regulations, travel restrictions and a decrease in passenger demand. One of the problems that must be resolved is how to organize and adapt flight schedules to current conditions by focusing on mathematical models that are used to optimize or increase the efficiency of managing flight schedules or air traffic. Mathematical models can help find ways to optimize the use of available resources, such as airport capacity, flight routes and flight frequency. The results obtained in this research are a mathematical model that specifically takes into account the variables involved in setting air traffic schedules during the COVID-19 pandemic so that capacity limits at airports and airspace are always normal. An optimization model was developed from previous research, namely the model that takes into account ground and air delays as well as the use of alternative paths and avoids deviations from the initial more accurate flight plan, which overall indicates that the maximum time and maximum distance values for each item have been optimized to achieve better values. This research has the novelty of producing a mathematical model using variables, objective functions, capacity limits, flight structure limits and variable domains, which then produces an algorithm with data input processes, determining optimization models, determining variables, determining objective functions, determining problems. The results of this model can be recommended to airlines in scheduling flights during the pandemic
... The research in [13] presents a digital system for assistance of pilot in prediction of air conflicts and it gives a platform for effective flight safety awareness through utilizing aircraft surveillance data. The dataset used in this study is large-scale which comprises of real time air traffic data including aircrafts conflicts data as well. ...
Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.
... 'Big data' is a prerequisite for the successful use of A.I. since ATM is powered by air to ground and ground to ground data flows. Air Traffic Control (ATC) is a vast and enormously complex domain which is performed in a centralized way to monitor, manage, and modify trajectory of each aircraft to ensure the critical safety of all flights under any circumstances while getting them safely to their destinations [31,38,39]. Meanwhile, ATC aims to use efficiently different resources available such as the airspace, the air routes, the airport, and the runways. ...
In this chapter, we will review some of the Artificial Intelligence (A.I.) roadmaps to discuss the various implications of A.I. on the aviation sector and we will identify some high-level objectives to be met and the roadmap actions suggested to respond to the growth of Aviation in Vietnam after a comparison between Vietnam and other countries. In aviation, the possibilities of A.I. will increase due to the increase in the processing power of the computers. It will be used to make autonomous flights, preventive maintenance, A.T.M. (Air Traffic Management) optimization, pilots, cabin crew, ground staff, and airport staff training possible in a cost saving, less time consuming and less polluting way. Ethics International discussions on A.I. have intensified after Japan’s G7 Presidency put the topic high on the agenda in 2016. The development in A.I. has international interconnections in terms of data circulation, algorithmic development and research investments. In this context, we will analyze this approach to the global stage as it is intended to build a consensus on a human-centric A.I. Through academic and professional expertise in the aviation and A.I. field, we intend to contribute to the needed high innovation capacity, skills and training development and management of A.I., supported by appropriate regulations and attention to ethical problems.
... The article [6] proposes using long short-term memory neural networks to analyze aircraft surveillance data to detect conflicts and provide air traffic controllers with improved situational awareness to transform air traffic management into a more automated, efficient, and safer next-generation system. ...
Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) to transform operations and improve efficiency and safety. However, the aviation industry requires qualified graduates with relevant AI competencies to meet this demand. This study analyzed aviation engineering bachelor’s programs at European universities to determine if they are preparing students for AI integration in aviation by incorporating AI-related topics. The analysis focused on program descriptions and syllabi using semantic annotation. The results showed a limited focus on AI and machine learning competencies, with more emphasis on foundational digital skills. Reasons include the newness of aviation AI, its specialized nature, and implementation challenges. As the industry evolves, dedicated AI programs may emerge. But currently, curricula appear misaligned with stated industry goals for AI adoption. The study provides an analytical methodology and competency framework to help educators address this gap. Producing graduates equipped with AI literacy and collaboration skills will be key to aviation’s intelligent future.
... The idea would be to completely eliminate the human operator from the tower environment and thus the need for an out-the-window view. Such developments are in fact a part of current air traffic management (ATM) research, perhaps a bit less prominent for conventional manned traffic operations, but certainly in the area of unmanned traffic management [6]. Nevertheless, for now, they should be seen in the context of research on automation work that still needs to progress in several areas of human perception and cognition before safe and efficient operations are achieved. ...
In recent years Augmented Reality (AR) has become one of the major focus points of user interface development. With the rapidly increasing computing power and developments in software and hardware applications during the last two decades, it has moved from theoretical approaches towards industry-wide application and mass production. The Royal Netherlands Aerospace Centre, NLR, tested several devices in the past, but only recent developments made it possible to effectively use them in an Air Traffic Control (ATC) working environment for Aerodrome Control Towers. In 2021 NLR carried out innovative technology experiments on their high-fidelity real-time air traffic control simulation and validation platform, NARSIM. These experiments were part of the SESAR 2020 project Digital Technologies for Tower (DTT) and focused on advanced HMI interaction modes for aerodrome tower controllers. A proposed Attention Capturing and Guidance concept with an AR device was evaluated inside an aerodrome control tower environment for Amsterdam Airport Schiphol. This paper reflects on the technology development activities that took place at NLR during the last decade and describes the different steps taken to apply the technology in a conventional control tower environment. It is shown that the recent technology developments must be seen as a big step forward in practical application of AR devices for ATC. Furthermore, an outlook into the expected future use of AR devices in conventional control tower environments will be given that goes beyond abovementioned concept elements. This outlook considers additional developments for standardization of digitized airport information and communication between different stakeholders and general performance improvements for AR devices.
... In currently used automated systems the amount of information to be processed by the operators accumulates, making them increasingly complex (Endsley and Kiris, 1995;Ortner et al., 2022;Li and Harris, 2007). Early studies have shown that automation-induced complexity leads to more cognitive load and poor performance associated with loss of situation awareness (Endsley and Kiris, 1995). ...
This research aims to comprehensively analyze the most essential uses of artificial intelligence in Aerospace Engineering. We obtained papers initially published in academic journals using a Systematic Quantitative Literature Review (SQLR) methodology. We then used bibliometric methods to examine these articles, including keyword co-occurrences and bibliographic coupling. The findings enable us to provide an up-to-date sketch of the available literature, which is then incorporated into an interpretive framework that enables AI's significant antecedents and effects to be disentangled within the context of innovation. We highlight technological, security, and economic factors as antecedents prompting companies to adopt AI to innovate. As essential outcomes of the deployment of AI, in addition to identifying the disciplinary focuses, we also identify business organizations' product innovation, process innovation, aerospace business model innovation, and national security issues. We provide research recommendations for additional examination in connection to various forms of innovation, drawing on the most critical findings from this study.
