Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges
Abstract
:1. Introduction
- A comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM);
- Future directions based on insights of single contributions and ATM solutions groups;
- An extensive list of open challenges in the context of Deep Learning (DL) applications in ATM;
- An extensive list of open challenges from the ATM solutions standpoint.
2. Background
2.1. Deep Learning (DL)
- Feed-Forward Networks: Feed-Forward Neural networks (also referred to as Deep Neural Networks—DNN—in this article) consists of neurons ordered into layers. The first layer, called the input layer, the last layer, called the output layer, and the hidden layers [14]. Neurons can be considered processing units connected to synaptic weights. These neurons produce an output using an activation function, which is sent to the following layer [15]. These networks are usually trained using the back-propagation algorithm (used to compute gradients) and the Stochastic Gradient Descent (SGD) algorithm to optimize the weights (using the gradient computed previously). Figure 1 illustrates a simple DNN and highlights the input, hidden, and output layers. The number of nodes and hidden layers can change depending on the problem faced, similar to the input vector. This architecture has been widely used and has presented tremendous success in several initiatives.
- Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) are a category of Deep Learning (DL) models designed to process data in a grid-like topology (e.g., time-series and image data). CNNs are usually composed of three types of layers: convolutional, pooling, and fully connected layers [16,17]. The convolutional layers are responsible for extracting important features. The pooling layers reduce the resolution of features, making them robust against noise and distortion. Finally, the fully-connected layers produce class scores from the activations [18]. Figure 2 illustrates a simple CNN model.
- Recurrent Neural Networks (RNN): Recurrent Neural Networks (RNNs) represent a neural network architecture used to detect patterns in sequences (e.g., images, text, or numerical time series) [19]. Important RNN features are the feedback connection and memory, which enable activations to flow in a loop and temporal processing [20]. Figure 3 illustrates a simple example of an RNN.
- Generative Adversarial Networks (GANs): This architecture is based on the competition between a generation and a discriminator. In this sense, the generator uses random noise to produce fake data while the discriminator tries to distinguish real data from fake data. When the generator can produce data that cannot be appropriately classified as fake by the discriminator, the model can produce realistic data [21,22]. Figure 4 illustrates a simple GAN architecture.
- Autoencoders (AE): This specific type of neural network was developed to encode inputs into a compressed and meaningful representation. After this reduced version of the provided features is produced, the model decodes it back, aiming to produce an output as close as possible to the input [23,24]. Figure 5 illustrates a simple AE architecture.
2.2. Air Traffic Management (ATM)
- ATM Operations, Architecture, Performance, and Validation (OAPV): focuses on solutions to enhance and enable trajectory-based operations, considering technologies related to aircraft trajectory. It may include trajectory planning [35,36], prediction [37,38], generation [39,40], optimization [41,42], and clustering [43,44];
- Advanced Air Traffic Services (AATS): involves tools to improve departure and arrival processes, separation management, air and ground safety, and systems to support flight planning. This area refers to solutions considering the interaction between humans and computers and may include augmentation solutions [51,52] and behavioral technologies [53]. Moreover, it also considers airspace complexity solutions [54], e.g., initiatives related to complexity estimation [55] and reduction [56,57] are examples of solutions in this portfolio.
3. Literature Review
- Year: Describes the year in which the article was published;
- ATM Area: Categorizes the article into one of the four ATM solutions areas previously described, i.e., OAPV, EAI, HPAO, and AATS;
- ATM System: Indicates if the solution focuses on Air Traffic Services (ATS), Airspace Management (ASM), or Air Traffic Flow Management (ATFM);
- Flight solution: Indicates if the solution is directly applicable to one (S) or multiple (M) aircraft;
- Deep Learning (DL) Application: Refers to the aspects of the Deep Learning (DL) application, indicating if the authors presented details on the architecture (Arc), validation (Val), and deployment (Dp);
- Airspace Key Performance Indicator (KPI): Indicates the main target of the proposed method regarding airspace operations. Works contributions are classified into safety (Sft), efficiency (Ef), and sustainability (Sus). Although some initiatives overlap multiple KPIs, we intend to identify the primary focus;
- Air Traffic Controller (ATCo): Indicates if the proposed solution is intended to support the operation of ATC professionals. This attribute identifies if the solution proposed considers human factors (HF—e.g., mental workload and fatigue identification) and augmentation capabilities (Aug—e.g., indicates if the solution indents to help professionals in the task).
3.1. Applications of Deep Neural Networks (DNN) Networks in ATM
3.2. Applications of Convolutional Neural Networks (CNN) in ATM
3.3. Applications of Recurrent Neural Networks (RNN) in ATM
3.4. Applications of Generative Adversarial Networks (GANs) in ATM
3.5. Applications of Autoencoders in ATM
Model | Paper | Year | ATM Area | ATC System | Flight Solution | DL Application | Airspace KPI | ATCo | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ATS | ASM | ATFM | S | M | Arc | Val | Dp | Sft | Ef | Sus | HF | Aug | ||||
CNN | Malekzadeh et al. [84] | 2017 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||
Liu et al. [85] | 2018 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Yang et al. [92] | 2019 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Liu et al. [97] | 2019 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Chen et al. [98] | 2019 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Van et al. [88] | 2019 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||
Qu et al. [87] | 2020 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Van et al. [89] | 2020 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||
Lin et al. [95] | 2020 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||
Pang et al. [82] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Zeng et al. [91] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Xie et al. [86] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Di et al. [83] | 2022 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Mas et al. [90] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Jardines et al. [93] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Rahman et al. [96] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
DNN | Horiguchi et al. [68] | 2017 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||
Kistan et al. [74] | 2018 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||
Boggavarapu et al. [77] | 2019 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Chakrabarty et al. [73] | 2019 | HPAO | ✔ | ✔ | ✔ | ✔ | ||||||||||
Mollinga et al. [62] | 2020 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Wang et al. [66] | 2020 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Mas et al. [72] | 2020 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||
Yazdi et al. [71] | 2020 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Jimenez et al. [79] | 2020 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Cheevachaipimol et al. [67] | 2021 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Bala et al. [69] | 2021 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Dong et al. [78] | 2021 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||
Gholami et al. [70] | 2022 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Tan et al. [75] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Ivanoska et al. [76] | 2022 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Sangeetha et al. [80] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||
Çakıcı et al. [81] | 2022 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Perez et al. [63] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
RNN | Shi et al. [104] | 2018 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Pang et al. [101] | 2019 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Zhao et al. [109] | 2019 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Shi et al. [100] | 2020 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Ma et al. [105] | 2020 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Jarry et al. [112] | 2020 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Shi et al. [111] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Shu et al. [106] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ||||||||||
Xu et al. [107] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Yan et al. [108] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Mas et al. [102] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Lim et al. [99] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Huang et al. [103] | 2022 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Asirvadam et al. [110] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
GAN | Zhang et al. [122] | 2018 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Fu et al. [117] | 2019 | EAI | ✔ | ✔ | ✔ | ✔ | ||||||||||
Pang et al. [114] | 2020 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Rahnemoonfar et al. [118] | 2020 | AATS | ✔ | ✔ | ✔ | |||||||||||
Aksoy et al. [115] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Pham et al. [116] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Guo et al. [120] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Olive et al. [123] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ||||||||||
Lang et al. [125] | 2021 | EAI | ✔ | ✔ | ✔ | ✔ | ||||||||||
Jarry et al. [124] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Wu et al. [113] | 2022 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Hu et al. [119] | 2022 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Huang et al. [121] | 2023 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
AE | Olive et al. [127] | 2018 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||
Chen et al. [134] | 2018 | HPAO | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Xuyun et al. [131] | 2019 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Fernandez et al. [132] | 2019 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Que et al. [130] | 2019 | EAI | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Wu et al. [128] | 2019 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||
Memarzadeh et al. [136] | 2020 | Aircraft | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Olive et al. [138] | 2020 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Corrado et al. [133] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
Zeng et al. [137] | 2021 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Kim et al. [139] | 2021 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Bastas et al. [126] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||
Chevrot et al. [129] | 2022 | OAPV | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
Wu et al. [135] | 2022 | AATS | ✔ | ✔ | ✔ | ✔ |
3.6. Further Insights: Opportunities
3.7. Paper Count and Keywords
3.8. ATM Solutions
4. Open Challenges
4.1. Deep Learning (DL) Applications
- Interpretability: Understanding the decision made by the DL models is paramount for safe and efficient operations. Hence, the proposal of strategies to explain the decision made in the full ATM system spectrum is a challenging but necessary step for future operations;
- Sustainability: From all works analyzed in this research, none of them are focused on sustainability services for future ATM systems. The use of DL techniques to foster the development of a new strategy is critical for future operations;
- Cybersecurity: Data are shared throughout the airspace systems in today’s operations, and future applications will require even more data. In this sense, it is critical that new solutions use advanced techniques (e.g., DL) to detect and mitigate cybersecurity threats;
- Urban Air Mobility (UAM): Several of the points discussed in this research are also applicable to new transportation paradigms, e.g., UAM. For example, DL can enable the development of trajectory-based solutions tailored to the UAM environment. In fact, lessons learned in the National Airspace System (NAS) can provide insightful directions for UAM applications [140,141];
- Deployment: Few of the works reviewed in this research focus on the deployment of such solutions. Besides the complexity that developing a DL application involves, deployment is also challenging and requires coordination with several stakeholders. Indeed, solutions to simplify the deployment of such methods are part of the open challenges.
4.2. ATM Solutions
4.2.1. Advanced Air Traffic Services (AATS)
- Temporal effects: Throughout the daily operations, the airspace state changes several times. However, there is an intrinsic temporal dependence in the evolution of the airspace state. In this sense, initiatives can focus on identifying temporal connections to improve the complexity prediction capabilities;
- Integrated Analysis: The complex airspace ecosystem entails various systems operating simultaneously. In this sense, an open challenge refers to using global resources and information to understand how complexity is impacted and, ultimately, develop more accurate complexity-based solutions;
- Disruption Management: The consideration of rare but disruptive events is vital in the assessment and development of new complexity-based solutions. Therefore, new capabilities for predicting rare events are part of the future ATM solutions portfolio.
- Data Collection: Building up datasets for training models is a complex challenge due to several factors. The development of new techniques to easily collect data without compromising the operation will produce valuable resources for new applications;
- Integrated Augmentation: Although some initiatives focus on supporting professionals in the ATM system, they are commonly separated and not part of the same portfolio. The development of a scalable and integrated approach to be used as a baseline across the ATM system is an open challenge;
- Training Frameworks: The inclusion of new technologies into the National Airspace System (NAS) requires several phases of testing and certification. In this sense, proposing new training approaches to simplify the use of these new technologies is necessary.
4.2.2. High-Performing Airport Operations (HPAO)
- Airport Data Collection: Although following the same rules, airports operate differently due to several factors (e.g., size, number of gates, and number of flights). Collecting internal data to improve the airport performance is a pillar for future solutions, and the development of a new data collection strategy is essential;
- Data Sampling: The current difficulty faced in collecting the data demands methods to generate realistic samples. In this case, there is a need for new sampling methods that cover the characteristics of the airport operation;
- Disruption Management: The impacts of disruption in delays is difficult to predict due to the minimal number of occurrences in history. Future solutions are required to handle this imbalanced environment and accurately predict such events.
4.2.3. ATM Operations, Architecture, Performance, and Validation (OAPV)
- New Aircraft Concepts: There are some companies working on the production of supersonic aircraft that will be integrated into the NAS in the near future. Then, new trajectory prediction services are required to attend to the new flight configurations and capabilities (both in terms of performance and regulations);
- Multi-Modal Analysis: Trajectory-based solutions are integrated into a complex ecosystem composed of several subsystems. In this sense, using data from different sources and configurations (e.g., audio and video) represents another open challenge;
- Disruption Management: Rare events are difficult to predict accurately. The development of new methods capable of estimating when disruptive events happen and how they affect the aircraft trajectory needs investigation.
4.2.4. Enabling Aviation Infrastructure (EAI)
- Data Collection: For several reasons, collecting data from aircraft (e.g., engines) is a complex task. Therefore, the development of new software and hardware technologies for data collection will improve the results obtained by the existing and future ATM solutions;
- Integrated Health Analysis: The complex ecosystem composed of several subsystems that surround the aircraft can provide information for in-flight decision-making. In this sense, integrating the in-flight solutions with the ecosystem can yield valuable resources and is in the scope of future works;
- New Aircraft Concepts: As new aircraft operate in the NAS (e.g., supersonic aircraft), flight parameters are expected to differ. Then, adjusting the existing solutions for such an environment is pivotal for efficient operations.
4.2.5. Integrated Solutions (IS)
- Collaborative Decision: Considering that local actions can change the global airspace mesh, solutions to enable safe, rapid, and efficient collaborative decision-making represent a significant advancement in today’s technologies. However, reaching this flawless collaboration is not simple, as it represents a research field in ATM systems.
- DL Orchestration: The use of DL solutions in several areas of ATC can improve efficiency. However, enabling these separated entities to communicate and share resources (e.g., parameters and outputs) can provide a smooth integration experience. However, the orchestration of such systems is complex and represents an open challenge.
- Knowledge sharing: Data belonging to different organizations is not always shared due to several reasons (e.g., privacy). Then, new privacy-preserving techniques (e.g., privacy-preserving transfer learning) can overcome this obstacle and enable DL applications to be more accurate without compromising privacy.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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---|---|---|---|---|
AATS | Yang et al. [92] | 2019 | CNN | Flow prediction in severe and rare weather conditions |
Liu et al. [97] | 2019 | CNN | Application of proposal in a real scenario | |
Van et al. [88] | 2019 | CNN | Automatic generation of training data consisting of solution space diagram (SSD) images and conflict resolutions | |
Xie et al. [86] | 2021 | CNN | Application of visual-based techniques in other ATM solutions | |
Jardines et al. [93] | 2022 | CNN | Investigate temporal relationships in weather data | |
Rahman et al. [96] | 2022 | CNN | Inclusion of airspace information to improve conflict resolution | |
Wang et al. [66] | 2020 | DNN | Application of different DL methods to improve performance | |
Tan et al. [75] | 2022 | DNN | Use of Deep Active Learning in other ATM solutions | |
Perez et al. [63] | 2022 | DNN | Application of proposal in different sector types | |
Shi et al. [111] | 2021 | RNN | Quantification of the uncertainty in the predictions | |
Shu et al. [106] | 2021 | RNN | Consideration of special events (e.g., weather and large activities) | |
Yan et al. [108] | 2021 | RNN | Use of other factors to improve prediction accuracy (e.g., weather information, ATC information, the influence of international flights, and dynamic traffic movements on the network. | |
Mas et al. [102] | 2021 | RNN | Implementation of visual framework to apply theoretical regulations and create feedback to re-train the existing model. | |
Lim et al. [99] | 2022 | RNN | Analysis of severe weather impacts on airports | |
Asirvadam et al. [110] | 2022 | RNN | Airspace optimization considering workload, weather and unplanned traffic | |
Zhang et al. [122] | 2018 | GAN | Unsupervised classification method to remove the need for data labeled with type information | |
Rahnemoonfar et al. [118] | 2020 | GAN | Simulation of other ATM systems (e.g., audio) | |
Huang et al. [121] | 2023 | GAN | Use of attention model mechanisms | |
Corrado et al. [133] | 2021 | AE | Hyperparameter Optimization | |
Kim et al. [139] | 2021 | AE | Inclusion of altitude in the proposed method | |
Wu et al. [135] | 2022 | AE | Use of more advanced classifiers | |
Van et al. [89] | 2020 | CNN | Model Optimization | |
Lin et al. [95] | 2020 | CNN | Increase data diversity | |
Mas et al. [90] | 2022 | CNN | Inclusion of additional input features to improve performance | |
Mollinga et al. [62] | 2020 | DNN | Inclusion of stochastic variables like weather, addition of waypoints, and change the simulation approach | |
Mas et al. [72] | 2020 | DNN | Use of an hybrid DL model | |
Sangeetha et al. [80] | 2022 | DNN | Consideration of rare events (e.g., weather-based events) | |
Pham et al. [116] | 2021 | GAN | Implementation of a multi-agent environment | |
Olive et al. [127] | 2018 | AE | Consideration of more evolved structures of networks | |
Wu et al. [128] | 2019 | AE | Application of similar strategies considering ATC professionals | |
Bastas et al. [126] | 2022 | AE | Improvements of predictions regarding low-level ATCOs’ conflict resolution actions | |
OAPV | Liu et al. [85] | 2018 | CNN | Extension of proposed algorithm to more features (e.g., ATM initiatives) |
Pang et al. [82] | 2021 | CNN | Consideration of rare events (e.g., weather-based events) | |
Zeng et al. [91] | 2021 | CNN | Approaches to handle loss of information from data normalization. | |
Jimenez et al. [79] | 2020 | DNN | Use of a more extensive dataset | |
Çakıcı et al. [81] | 2022 | DNN | Data sharing approaches considering aircraft and ATC | |
Shi et al. [104] | 2018 | RNN | Use of multi-modal data, including images, audios and videos | |
Pang et al. [101] | 2019 | RNN | Consideration of rare events (e.g., weather-based events) | |
Zhao et al. [109] | 2019 | RNN | Application of D-LSTM to trajectory information prediction in high density airspace | |
Shi et al. [100] | 2020 | RNN | Inclusion of of multi-modal data | |
Ma et al. [105] | 2020 | RNN | Models for long-term 4D trajectory prediction | |
Xu et al. [107] | 2021 | RNN | Integration of meteorological conditions to achieve more accurate and stable trajectory prediction | |
Huang et al. [103] | 2022 | RNN | Models that use the combination of weather, control, and other uncertainties. | |
Pang et al. [114] | 2020 | GAN | Development of models to improve the prediction performance | |
Aksoy et al. [115] | 2021 | GAN | Consideration of rare events (e.g., weather-based events) | |
Guo et al. [120] | 2021 | GAN | Inclusion of new features to to further improve the performance of the proposed model | |
Olive et al. [123] | 2021 | GAN | Application of the proposed method to compare data-driven trajectory generation models | |
Jarry et al. [124] | 2021 | GAN | Analysis of tailored network architectures and learning | |
Wu et al. [113] | 2022 | GAN | Application of the propose method for short-term trajectory prediction and air traffic state estimation. | |
Hu et al. [119] | 2022 | GAN | To adopt this approach in other ATM solutions | |
Olive et al. [138] | 2020 | AE | Impact assess of clustering losses on the performance of reconstruction-based anomaly detection methods. | |
Zeng et al. [137] | 2021 | AE | Use of the proposed model to assist trajectory prediction solutions | |
Chevrot et al. [129] | 2022 | AE | To adopt this approach in other domains | |
EAI | Malekzadeh et al. [84] | 2017 | CNN | Use of other DNN architecture for this application |
Chen et al. [98] | 2019 | CNN | Consideration of rare events (e.g., weather-based events) | |
Di et al. [83] | 2022 | CNN | Consideration of rare events (e.g., weather-based events) | |
Dong et al. [78] | 2021 | DNN | Data Collection, Labeling, and transfer Learning | |
Jarry et al. [112] | 2020 | RNN | Enhancing flap and landing gear setting prediction with airspeed information | |
Fu et al. [117] | 2019 | GAN | To adopt this approach in other ATM solutions | |
Lang et al. [125] | 2021 | GAN | To adopt this approach in other ATM solutions | |
Xuyun et al. [131] | 2019 | AE | Use of more com prehensive fault cases to locate the fault source | |
Fernandez et al. [132] | 2019 | AE | Consideration of other airspace aspects (e.g., sector desity) | |
Que et al. [130] | 2019 | AE | Automate rapid development of efficient anomaly detection on FPGAs for various applications | |
HPAO | Qu et al. [87] | 2020 | CNN | Use of new models to improve results |
Horiguchi et al. [68] | 2017 | DNN | Inclusion of reservation data in the analysis | |
Boggavarapu et al. [77] | 2019 | DNN | Use of more airport data | |
Chakrabarty et al. [73] | 2019 | DNN | Use of advanced preprocessing and sampling techniques | |
Yazdi et al. [71] | 2020 | DNN | Consideration of rare events (e.g., weather-based events) | |
Cheevachaipimol et al. [67] | 2021 | DNN | Use of other methods to handle imbalanced data | |
Bala et al. [69] | 2021 | DNN | Use of DNN in aircraft maintenance | |
Gholami et al. [70] | 2022 | DNN | Consideration of rare events (e.g., weather-based events) | |
Ivanoska et al. [76] | 2022 | DNN | Consideration of other aspects in flights (e.g., aircraft usage) | |
Chen et al. [134] | 2018 | AE | Consideration of rare events (e.g., weather-based events) |
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Share and Cite
Pinto Neto, E.C.; Baum, D.M.; Almeida, J.R.d., Jr.; Camargo, J.B., Jr.; Cugnasca, P.S. Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges. Aerospace 2023, 10, 358. https://doi.org/10.3390/aerospace10040358
Pinto Neto EC, Baum DM, Almeida JRd Jr., Camargo JB Jr., Cugnasca PS. Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges. Aerospace. 2023; 10(4):358. https://doi.org/10.3390/aerospace10040358
Chicago/Turabian StylePinto Neto, Euclides Carlos, Derick Moreira Baum, Jorge Rady de Almeida, Jr., João Batista Camargo, Jr., and Paulo Sergio Cugnasca. 2023. "Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges" Aerospace 10, no. 4: 358. https://doi.org/10.3390/aerospace10040358