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16 pages, 471 KiB

Open AccessArticle

A Methodology for Assessing Capacity of the Terminal Maneuvering Area Based on Service Resource Equilibrium

by Qifeng Mou, Ze Yang and Liming Zhang

Aerospace 2023, 10(10), 894; https://doi.org/10.3390/aerospace10100894 - 19 Oct 2023

Viewed by 1636

Abstract

To effectively estimate and optimize the airport terminal maneuvering area throughput based on the equilibrium of air traffic service resource supply and demand, this research proposes an approach to assess terminal maneuvering area capacity from the perspective of air traffic service resource availability. [...] Read more.

To effectively estimate and optimize the airport terminal maneuvering area throughput based on the equilibrium of air traffic service resource supply and demand, this research proposes an approach to assess terminal maneuvering area capacity from the perspective of air traffic service resource availability. Terminal maneuvering area capacity is optimized based on the equilibrium of air traffic service resource supply and demand. The supply–demand nexus is examined in consideration of terminal maneuvering area route structure, traffic flow characteristics, and safety regulations. A flight service probability matrix and a terminal maneuvering area demand and supply service time model are constructed to quantify resource expenditure at varied capacity levels. An optimization model is then developed to allocate the airport resources effectively, fully utilizing the capacity to provide maximal outputs under resource limitations. Model computation and simulation results demonstrate the deviation between estimated and amended capacities is under 0.3 flight sorties per hour. The outcomes are congruent with historical statistics, thereby validating the accuracy and reliability of the model proposed in this study. Given capacity parameters, the model can deduce the maximal aircraft quantity served concurrently in terminal maneuvering areas during peak periods. These revelations indicate that the submitted model furnishes theoretical foundation and reference for terminal maneuvering area sector partition and traffic alerting. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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13 pages, 2978 KiB

Open AccessArticle

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

by Jie Li, Di Shen, Fuping Yu and Renmeng Zhang

Aerospace 2023, 10(9), 758; https://doi.org/10.3390/aerospace10090758 - 27 Aug 2023

Cited by 5 | Viewed by 1567

Abstract

With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the [...] Read more.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the flight behavior of UAVs has become an urgent and imperative issue that needs to be addressed. Hence, this paper proposes a standardized approach to UAV flight through the design of an air channel network. The air channel network comprises numerous single air channels, and this study focuses on investigating the characteristics of a single air channel. To achieve optimal outcomes, the concept of the artificial potential field algorithm is integrated into the deep Q-learning algorithm during the establishment of a single air channel. By improving the action space and reward mechanism, the resulting single air channel enables efficient avoidance of various buildings and obstacles. Finally, the algorithm is assessed through comprehensive simulation experiments, demonstrating its effective fulfillment of the aforementioned requirements. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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22 pages, 1491 KiB

Open AccessArticle

Airspace Designs and Operations for UAS Traffic Management at Low Altitude

by Ui-Jeong Lee, Sang-Jun Ahn, Dong-Young Choi, Sang-Min Chin and Dae-Sung Jang

Aerospace 2023, 10(9), 737; https://doi.org/10.3390/aerospace10090737 - 22 Aug 2023

Cited by 3 | Viewed by 2804

Abstract

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, [...] Read more.

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, in building a safe path, the UTM needs to determine the time and space allocated to each flight. Ideas for discretizing and structuring airspace in various forms have been proposed to enhance the efficiency of system operation and improve traffic congestion through effectual airspace allocation. Additionally, various methods of allocating UAVs to structured unit spaces have been studied in the literature. In this paper, the methods and structural designs for allocating airspace that have appeared in related studies are classified into several types, and their strengths and weaknesses are analyzed. The structured airspace designs are categorized into three models: Air-Matrix, Air-Network, and Air-Tube, and analyzed according to their sub-structures and temporal allocation methods. In addition, a quantitative analysis is conducted by re-categorizing the structured airspace and operation methods and building their combinations. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 989 KiB

Open AccessArticle

Adaptive IMM-UKF for Airborne Tracking

by Alvaro Arroyo Cebeira and Mariano Asensio Vicente

Aerospace 2023, 10(8), 698; https://doi.org/10.3390/aerospace10080698 - 7 Aug 2023

Cited by 2 | Viewed by 2032

Abstract

In this paper, we propose a nonlinear tracking solution for maneuvering aerial targets based on an adaptive interacting multiple model (IMM) framework and unscented Kalman filters (UKFs), termed as AIMM-UKF. The purpose is to obtain more accurate estimates, better consistency of the tracker, [...] Read more.

In this paper, we propose a nonlinear tracking solution for maneuvering aerial targets based on an adaptive interacting multiple model (IMM) framework and unscented Kalman filters (UKFs), termed as AIMM-UKF. The purpose is to obtain more accurate estimates, better consistency of the tracker, and more robust prediction during sensor outages. The AIMM-UKF framework provides quick switching between two UKFs by adapting the transition probabilities between modes based on a distance function. Two modes are implemented: a uniform motion model and a maneuvering model. The experimental validation is performed with Monte Carlo simulations of three scenarios with ACAS Xa tracking logic as a benchmark, which is the next generation of airborne collision avoidance systems. The two algorithms are compared using hypothesis testing of the root mean square errors. In addition, we determine the normalized estimation error squared (NEES), a new proposed noise reduction factor to compare the estimation errors against the measurement errors, and an estimated maximum error of the tracker during sensor dropouts. The experimental results illustrate the superior performance of the proposed solution with respect to the tracking accuracy, consistency, and expected maximum error. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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24 pages, 4064 KiB

Open AccessArticle

TCFLTformer: TextCNN-Flat-Lattice Transformer for Entity Recognition of Air Traffic Management Cyber Threat Knowledge Graphs

by Chao Liu, Buhong Wang, Zhen Wang, Jiwei Tian, Peng Luo and Yong Yang

Aerospace 2023, 10(8), 697; https://doi.org/10.3390/aerospace10080697 - 7 Aug 2023

Cited by 3 | Viewed by 1885

Abstract

With the development of the air traffic management system (ATM), the cyber threat for ATM is becoming more and more serious. The recognition of ATM cyber threat entities is an important task, which can help ATM security experts quickly and accurately recognize threat [...] Read more.

With the development of the air traffic management system (ATM), the cyber threat for ATM is becoming more and more serious. The recognition of ATM cyber threat entities is an important task, which can help ATM security experts quickly and accurately recognize threat entities, providing data support for the later construction of knowledge graphs, and ensuring the security and stability of ATM. The entity recognition methods are mainly based on traditional machine learning in a period of time; however, the methods have problems such as low recall and low accuracy. Moreover, in recent years, the rise of deep learning technology has provided new ideas and methods for ATM cyber threat entity recognition. Alternatively, in the convolutional neural network (CNN), the convolution operation can efficiently extract the local features, while it is difficult to capture the global representation information. In Transformer, the attention mechanism can capture feature dependencies over long distances, while it usually ignores the details of local features. To solve these problems, a TextCNN-Flat-Lattice Transformer (TCFLTformer) with CNN-Transformer hybrid architecture is proposed for ATM cyber threat entity recognition, in which a relative positional embedding (RPE) is designed to encode position text content information, and a multibranch prediction head (MBPH) is utilized to enhance deep feature learning. TCFLTformer first uses CNN to carry out convolution and pooling operations on the text to extract local features and then uses a Flat-Lattice Transformer to learn temporal and relative positional characteristics of the text to obtain the final annotation results. Experimental results show that this method has achieved better results in the task of ATM cyber threat entity recognition, and it has high practical value and theoretical contribution. Besides, the proposed method expands the research field of ATM cyber threat entity recognition, and the research results can also provide references for other text classification and sequence annotation tasks. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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33 pages, 5783 KiB

Open AccessArticle

A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival

by Zhe Zheng, Bo Zou, Wenbin Wei and Wen Tian

Aerospace 2023, 10(8), 675; https://doi.org/10.3390/aerospace10080675 - 28 Jul 2023

Cited by 1 | Viewed by 2166

Abstract

The ability to accurately predict flight time of arrival in real time during a flight is critical to the efficiency and reliability of aviation system operations. This paper proposes a data-light and trajectory-based machine learning approach for the online prediction of estimated time [...] Read more.

The ability to accurately predict flight time of arrival in real time during a flight is critical to the efficiency and reliability of aviation system operations. This paper proposes a data-light and trajectory-based machine learning approach for the online prediction of estimated time of arrival at terminal airspace boundary (ETA_TAB) and estimated landing time (ELDT), while a flight is airborne. Rather than requiring a large volume of data on aircraft aerodynamics, en-route weather, and traffic, this approach uses only flight trajectory information on latitude, longitude, and speed. The approach consists of four modules: (a) reconstructing the sequence of trajectory points from the raw trajectory that has been flown, and identifying its best-matched historical trajectory which bears the most similarity; (b) predicting the remaining trajectory, based on what has been flown and the best-matched historical trajectory; this is achieved by developing a long short-term memory (LSTM) network trajectory prediction model; (c) predicting the ground speed of the flight along its predicted trajectory, iteratively using the current position and previous speed information; to this end, a gradient boosting machine (GBM) speed prediction model is developed; and (d) predicting ETA_TAB using trajectory and speed prediction from (b) and (c), and using ETA_TAB to further predict ELDT. Since LSTM and GBM models can be trained offline, online computation efforts are kept at a minimum. We apply this approach to real-world flights in the US. Based on our findings, the proposed approach yields better prediction performance than multiple alternative methods. The proposed approach is easy to implement, fast to perform, and effective in prediction, thus presenting an appeal to potential users, especially those interested in flight ETA prediction in real time but having limited data access. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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20 pages, 885 KiB

Open AccessArticle

Extraction of CD&R Work Phases from Eye-Tracking and Simulator Logs: A Topic Modelling Approach

by Aida Nordman, Lothar Meyer, Karl Johan Klang, Jonas Lundberg and Katerina Vrotsou

Aerospace 2023, 10(7), 595; https://doi.org/10.3390/aerospace10070595 - 29 Jun 2023

Viewed by 1243

Abstract

Automation in Air Traffic Control (ATC) is gaining an increasing interest. Possible relevant applications are in automated decision support tools leveraging the performance of the Air Traffic Controller (ATCO) when performing tasks such as Conflict Detection and Resolution (CD&R). Another important area of [...] Read more.

Automation in Air Traffic Control (ATC) is gaining an increasing interest. Possible relevant applications are in automated decision support tools leveraging the performance of the Air Traffic Controller (ATCO) when performing tasks such as Conflict Detection and Resolution (CD&R). Another important area of application is in ATCOs’ training by aiding instructors to assess the trainees’ strategies. From this perspective, models that capture the cognitive processes and reveal ATCOs’ work strategies need to be built. In this work, we investigated a novel approach based on topic modelling to learn controllers’ work patterns from temporal event sequences obtained by merging eye movement data with data from simulation logs. A comparison of the work phases exhibited by the topic models and the Conflict Life Cycle (CLC) reference model, derived from post-simulation interviews with the ATCOs, indicated that there was a correspondence between the phases captured by the proposed method and the CLC framework. Another contribution of this work is a method to assess similarities between ATCOs’ work strategies. A first proof-of-concept application targeting the CD&R task is also presented. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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14 pages, 1723 KiB

Open AccessArticle

Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

by Aliaksei Pilko, Mario Ferraro and James Scanlan

Aerospace 2023, 10(7), 593; https://doi.org/10.3390/aerospace10070593 - 29 Jun 2023

Cited by 2 | Viewed by 1437

Abstract

Integration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. [...] Read more.

Integration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. Simulated probable traffic scenarios are superimposed on a desired Uncrewed Aircraft operation and then sampled using Monte Carlo methods. The results are used to estimate the operation-specific collision probability with known uncertainty in the output. The methodology is demonstrated for an example medical logistics operation in the United Kingdom, and a Target Level of Safety is used as a benchmark to decide whether the operation should be permitted. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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20 pages, 1292 KiB

Open AccessArticle

Cognitive Load Assessment of Air Traffic Controller Based on SCNN-TransE Network Using Speech Data

by Jing Yang, Hongyu Yang, Zhengyuan Wu and Xiping Wu

Aerospace 2023, 10(7), 584; https://doi.org/10.3390/aerospace10070584 - 23 Jun 2023

Cited by 2 | Viewed by 2282

Abstract

Due to increased air traffic flow, air traffic controllers (ATCs) operate in a state of high load or even overload for long periods of time, which can seriously affect the reliability and efficiency of controllers’ commands. Thus, the early identification of ATCs who [...] Read more.

Due to increased air traffic flow, air traffic controllers (ATCs) operate in a state of high load or even overload for long periods of time, which can seriously affect the reliability and efficiency of controllers’ commands. Thus, the early identification of ATCs who are overworked is crucial to the maintenance of flight safety while increasing overall flight efficiency. This study uses a comprehensive comparison of existing cognitive load assessment methods combined with the characteristics of the ATC as a basis from which a method for the utilization of speech parameters to assess cognitive load is proposed. This method is ultimately selected due to the minimal interference of the collection equipment and the abundance of speech signals. The speech signal is pre-processed to generate a Mel spectrogram, which contains temporal information in addition to energy, tone, and other spatial information. Therefore, a speech cognitive load evaluation model based on a stacked convolutional neural network (CNN) and the Transformer encoder (SCNN-TransE) is proposed. The use of a CNN and the Transformer encoder allows us to extract spatial features and temporal features, respectively, from contextual information from speech data and facilitates the fusion of spatial features and temporal features into spatio-temporal features, which improves our method’s ability to capture the depth features of speech. We conduct experiments on air traffic control communication data, which show that the detection accuracy and F1 score of SCNN-TransE are better than the results from the support-vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost), and stacked CNN parallel long short-term memory with attention (SCNN-LSTM-Attention) models, reaching values of 97.48% and 97.07%, respectively. Thus, our proposed model can realize the effective evaluation of cognitive load levels. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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16 pages, 2837 KiB

Open AccessArticle

Airport Cluster Delay Prediction Based on TS-BiLSTM-Attention

by Xiujie Wei, Yinfeng Li, Ranran Shang, Chang Ruan and Jingzhang Xing

Aerospace 2023, 10(7), 580; https://doi.org/10.3390/aerospace10070580 - 22 Jun 2023

Cited by 5 | Viewed by 1517

Abstract

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on [...] Read more.

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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19 pages, 5949 KiB

Open AccessArticle

Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets

by Qunyu Xu, Jia Liu, Min Su and Weishi Chen

Aerospace 2023, 10(5), 462; https://doi.org/10.3390/aerospace10050462 - 16 May 2023

Cited by 2 | Viewed by 1851

Abstract

Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of [...] Read more.

Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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22 pages, 5168 KiB

Open AccessArticle

Unifying Tactical Conflict Prevention, Detection, and Resolution Methods in Non-Orthogonal Constrained Urban Airspace

by Călin Andrei Badea, Andres Morfin Veytia, Niki Patrinopoulou, Ioannis Daramouskas, Joost Ellerbroek, Vaios Lappas, Vassilios Kostopoulos and Jacco Hoekstra

Aerospace 2023, 10(5), 423; https://doi.org/10.3390/aerospace10050423 - 30 Apr 2023

Viewed by 1848

Abstract

The use of small aircraft for a wide range of missions in urban airspace is expected to increase in the future. In Europe, efforts have been invested into developing a unified system, called U-space, to manage aircraft in dense very-low-level urban airspace. The [...] Read more.

The use of small aircraft for a wide range of missions in urban airspace is expected to increase in the future. In Europe, efforts have been invested into developing a unified system, called U-space, to manage aircraft in dense very-low-level urban airspace. The Metropolis II project aimed to research what degree of centralisation an air traffic management system should use in such airspace. The paper at hand is a follow-up, and investigates improvements that can be brought to the tactical conflict prevention, detection, and resolution module of such a system in order to harmonise these components with an organic high-density U-space environment. The proposed improvements are: the prioritisation of vertical conflict prevention in intersections, the use of intent in detecting and resolving conflicts, and the use of heading-based manoeuvres in open airspace. Results show that the use of intent information in the conflict detection process, as well as the implementation of suitable tactical prevention procedures, can greatly increase airspace safety. Furthermore, the experiments revealed that the effectiveness of conflict resolution algorithms is highly dependent on the airspace rules and structure. This reiterates the potential for increasing the safety and efficiency of operations within constrained airspace if the tactical separation modules are unified with the other components of air traffic management systems for U-space. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 13775 KiB

Open AccessArticle

Validating Dynamic Sectorization for Air Traffic Control Due to Climate Sensitive Areas: Designing Effective Air Traffic Control Strategies

by Nils Ahrenhold, Ingrid Gerdes, Thorsten Mühlhausen and Annette Temme

Aerospace 2023, 10(5), 405; https://doi.org/10.3390/aerospace10050405 - 26 Apr 2023

Cited by 3 | Viewed by 1735

Abstract

Dynamic sectorization is a powerful possibility to balance the controller workload with respect to traffic flows changing over time. A multi-objective optimization system analyzes the traffic flow over time and determines suitable time-dependent sectorizations. Our dynamic sectorization system is integrated into a radar [...] Read more.

Dynamic sectorization is a powerful possibility to balance the controller workload with respect to traffic flows changing over time. A multi-objective optimization system analyzes the traffic flow over time and determines suitable time-dependent sectorizations. Our dynamic sectorization system is integrated into a radar display as part of a working environment for air traffic controllers. A use case defining climate-sensitive areas leads to changes in traffic flows. When using the system, three controllers are assessed in two scenarios: the developed controller assistance system and the work in a dynamic airspace sectorization environment. We performed a concept validation in which we evaluated how controllers cope with sectors adapting to the traffic flow. The solution was rated as highly applicable by the involved controllers. The trials revealed the necessity to adapt the current procedures and define new aspects more precisely. In this paper, we present the developed environment and the theoretical background as well as the traffic scenarios. Furthermore, we describe the integration in an Air Traffic Management (ATM) environment and the questionnaires developed to assess the functionality of the dynamic sectorization approach. Finally, we present a proposal to enhance controller guidelines in order to cope with situations emerging from dynamic sectorizations, including naming conventions and phraseology. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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24 pages, 3586 KiB

Open AccessArticle

Pilot Selection in the Era of Virtual Reality: Algorithms for Accurate and Interpretable Machine Learning Models

by Luoma Ke, Guangpeng Zhang, Jibo He, Yajing Li, Yan Li, Xufeng Liu and Peng Fang

Aerospace 2023, 10(5), 394; https://doi.org/10.3390/aerospace10050394 - 25 Apr 2023

Cited by 8 | Viewed by 3268

Abstract

With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select suitable prospective pilots in a cost-efficient manner has become an important research question. In the current study, 23 pilots were recruited [...] Read more.

With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select suitable prospective pilots in a cost-efficient manner has become an important research question. In the current study, 23 pilots were recruited from China Eastern Airlines, and 23 novices were from the community of Tsinghua University. A novel approach incorporating machine learning and virtual reality technology was applied to distinguish features between these participants with different flight skills. Results indicate that SVM with the MIC feature selection method consistently achieved the highest prediction performance on all metrics with an accuracy of 0.93, an AUC of 0.96, and an F1 of 0.93, which outperforms four other classifier algorithms and two other feature selection methods. From the perspective of feature selection methods, the MIC method can select features with a nonlinear relationship to sampling labels instead of a simple filter-out. Our new implementation of the SVM + MIC algorithm outperforms all existing pilot selection algorithms and perhaps provides the first implementation based on eye tracking and flight dynamics data. This study’s VR simulation platforms and algorithms can be used for pilot selection, training, and personnel selection in other fields (e.g., astronauts). Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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44 pages, 6971 KiB

Open AccessArticle

Cost–Benefit Analysis of Investments in Air Traffic Management Infrastructures: A Behavioral Economics Approach

by Álvaro Rodríguez-Sanz and Luis Rubio Andrada

Aerospace 2023, 10(4), 383; https://doi.org/10.3390/aerospace10040383 - 20 Apr 2023

Cited by 3 | Viewed by 3788

Abstract

An important and challenging question for airport operators is the management of airport capacity and demand. Airport capacity depends on the available infrastructure, external factors, and operating procedures. Investments in Air Traffic Management (ATM) infrastructures mainly affect airside operations and include operational enhancements [...] Read more.

An important and challenging question for airport operators is the management of airport capacity and demand. Airport capacity depends on the available infrastructure, external factors, and operating procedures. Investments in Air Traffic Management (ATM) infrastructures mainly affect airside operations and include operational enhancements to improve the efficiency, reliability, and sustainability of airport operations. Therefore, they help increase capacity while limiting the impact on the airport infrastructure itself. By reviewing the neoclassical valuation principles for Cost–Benefit Analysis (CBA), we find that it does not consider relevant behavioral economic challenges to conventional analysis, particularly: failure of the expected utility hypotheses, dependence of valuations on reference points, and time inconsistency. These challenges are then incorporated through practical guidelines into the traditional welfare model to achieve a new methodology. We propose a novel CBA behavioral framework for investments in ATM infrastructures to help policy makers and airport operators when faced with a capacity development decision. This is complemented with a practical example to illustrate and test the applicability of the proposed model. The case study evaluates the deployment of Automatic Dependent Surveillance–Broadcast (ADS–B) as an investment aimed at improving ATM operational procedures in the airport environment by providing advanced ground surveillance data. This allows airport operators to discover the causes of taxi congestion and safety hotspots on the airport airside. The benefits of ADS–B are related to enhanced flight efficiency, reduced environmental impact, increased airport throughput, and improved operational predictability and flexibility, thus reducing waiting times. At the airport level, reducing the waiting times of aircraft on the ground would lead to a capacity release and a reduction in delays. The results show that, following a traditional CBA, the investment is clearly viable, with a strong economic return. Including behavioral notions allows us to propose a new evaluation framework that complements this conclusion with a model that also considers inconsistencies in time and risk perception. A positive Net Present Value can turn into a negative prospect valuation, if diminishing sensitivity and loss aversion are considered. This explains the reticent behavior of decision makers toward projects that require robust investments in the short-term, yet are slow to generate positive cash flows. Finally, we draw conclusions to inform policy makers about the effects of adopting a behavioral approach when evaluating ATM investments. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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13 pages, 3347 KiB

Open AccessArticle

A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol

by Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez and Georges Ghazi

Aerospace 2023, 10(4), 357; https://doi.org/10.3390/aerospace10040357 - 4 Apr 2023

Cited by 9 | Viewed by 1926

Abstract

This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was [...] Read more.

This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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28 pages, 5967 KiB

Open AccessArticle

Air Traffic Complexity Evaluation with Hierarchical Graph Representation Learning

by Lu Zhang, Hongyu Yang and Xiping Wu

Aerospace 2023, 10(4), 352; https://doi.org/10.3390/aerospace10040352 - 3 Apr 2023

Cited by 1 | Viewed by 2233

Abstract

Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air traffic complexity in a sector [...] Read more.

Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air traffic complexity in a sector were mostly based on aircraft operational status and lacked comprehensiveness of characterization and were less adaptable in real situations. To settle these issues, a deep learning technique grounded on complex networks was proposed, employing the flight conflict network (FCN) to generate an air traffic situation graph (ATSG), with the air traffic control instruction (ATCOI) received by each aircraft included as an extra node attribute to increase the accuracy of the evaluation. A pooling method with a graph neural network (GNN) was used to analyze the graph-structured air traffic information and produce the sector complexity rank automatically. The model Hierarchical Graph Representing Learning (HGRL) was created to build comprehensive feature representations which involve two parts: graph structure coarsening and graph attribute learning. Structure coarsening reduced the feature map size by choosing an adaptive selection of nodes, while attribute coarsening selected key nodes in the graph-level representation. The experimental findings of a real dataset from the Chinese aviation industry reveal that our proposed model exceeds prior methods in its ability to extract critical information from an ATSG. Moreover, our work could be applied in the two main types of sectors and without extra factor calculations to determine the complexity of the airspace. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 3799 KiB

Open AccessArticle

Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

by Weijun Pan, Peiyuan Jiang, Zhuang Wang, Yukun Li and Zhenlong Liao

Aerospace 2023, 10(4), 349; https://doi.org/10.3390/aerospace10040349 - 3 Apr 2023

Cited by 6 | Viewed by 2092

Abstract

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent [...] Read more.

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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17 pages, 3286 KiB

Open AccessArticle

Safety and Efficiency Evaluation Model for Converging Operation of Aircraft and Vehicles

by Kai Yang, Hongyu Yang, Jianwei Zhang and Rui Kang

Aerospace 2023, 10(4), 343; https://doi.org/10.3390/aerospace10040343 - 1 Apr 2023

Cited by 2 | Viewed by 1728

Abstract

To explore the mixed traffic characteristics of vehicles and aircraft on the airport surface and solve the problem of real-time conflict detection at key intersections. According to the actual taxiing procedures and airport control rules in China, this paper focus on abstracting the [...] Read more.

To explore the mixed traffic characteristics of vehicles and aircraft on the airport surface and solve the problem of real-time conflict detection at key intersections. According to the actual taxiing procedures and airport control rules in China, this paper focus on abstracting the mixed motion process of aircraft and vehicles in the maneuvering area, defining the convergent cross-safety operation scenario. To quantify the driver’s attention to safety separation and the degree of conservatism in adjusting speed, the vehicle deceleration rate and acceleration rate are defined with α as the exponent. Under the same spacing, the vehicle deceleration rate is directly proportional to α, and α is named the vehicle safety sensitivity. At the same time, the rules of speed change of vehicles and aircraft will be designed, and a convergent operation model of aircraft and vehicles will be proposed. Based on real-time speed and separation dynamic assessment of safety and efficiency under different traffic strategies, the computer simulation results show that vehicle safety sensitivity and deceleration rules determine the sequence of vehicles and aircraft when they are passing through in the short term and can affect the proportion of mixed traffic flow in the long run. The safety probability of vehicles passing the intersection first is negatively correlated with vehicle safety sensitivity, while the safety probability of aircraft passing the intersection first is correlated positively with vehicle safety sensitivity. The efficiency of passing without conflict with mixed traffic has an inverse relationship with vehicle safety sensitivity. When the vehicle safety sensitivity takes the value in the interval [0.4, 0.6], a mixed traffic flow with higher safety and efficiency, better stability, and a balanced ratio of locomotives can be obtained. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 409 KiB

Open AccessArticle

Study of Delay Prediction in the US Airport Network

by Kerim Kiliç and Jose M. Sallan

Aerospace 2023, 10(4), 342; https://doi.org/10.3390/aerospace10040342 - 1 Apr 2023

Cited by 4 | Viewed by 3365

Abstract

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays [...] Read more.

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 4513 KiB

Open AccessArticle

Performance-Based Navigation Approach Procedures with Barometric Vertical Guidance: How to Select the Air Temperature for Approach Procedure Design

by Luis Pérez Sanz, Carmen Martínez García-Gasco, Marta Pérez Maroto, Javier A. Pérez-Castán, Lidia Serrano-Mira and Víctor Fernando Gómez Comendador

Aerospace 2023, 10(4), 337; https://doi.org/10.3390/aerospace10040337 - 28 Mar 2023

Cited by 1 | Viewed by 2286

Abstract

In performance-based navigation (PBN) procedures with barometric vertical guidance, the effective vertical path angle (VPA) depends on the actual air temperature at the time of approach execution. A very low design temperature could result in an obstacle clearance height (OCH) higher than needed; [...] Read more.

In performance-based navigation (PBN) procedures with barometric vertical guidance, the effective vertical path angle (VPA) depends on the actual air temperature at the time of approach execution. A very low design temperature could result in an obstacle clearance height (OCH) higher than needed; hence, the airport throughput could be reduced when the cloud ceiling is below the OCH. Conversely, the design of a low temperature higher than is practical could lead to long periods in which the procedure cannot be used. The results of this research show that there is not much difference between the effective VPA for the different low temperatures studied. However, this slight difference, when obstacles penetrating the final approach surface (FAS) exist, usually leads to the approach minima being significantly different from each other. The objective of this study was to analyse the impact of the selected designed low temperatures in PBN procedures with barometric vertical guidance on the OCH/runway throughput and approach periods of use balance. Finally, guidelines on the selection of the minimum designed low temperature are proposed. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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19 pages, 2142 KiB

Open AccessArticle

Study on Characteristics and Invulnerability of Airspace Sector Network Using Complex Network Theory

by Haijun Liang, Shiyu Zhang and Jianguo Kong

Aerospace 2023, 10(3), 225; https://doi.org/10.3390/aerospace10030225 - 25 Feb 2023

Cited by 4 | Viewed by 1933

Abstract

The air traffic control (ATC) network’s airspace sector is a crucial component of air traffic management. The increasing demand for air transportation services has made limited airspace a significant challenge to sustainable and efficient air transport operations. To address the issue of traffic [...] Read more.

The air traffic control (ATC) network’s airspace sector is a crucial component of air traffic management. The increasing demand for air transportation services has made limited airspace a significant challenge to sustainable and efficient air transport operations. To address the issue of traffic congestion and flight delays, improving the operational efficiency of ATC has been identified as a key strategy. A clear understanding of the characteristics of airspace sectors, which are the building blocks of ATC, is essential for optimizing air traffic management. In this research, a novel approach using complex network theory was applied to examine the features and invulnerability of the airspace sector network. We developed a model of the airspace sector network by treating air traffic control sectors as network nodes and the flow of air traffic between these sectors as edges. Network characteristics were analyzed using several metrics including degree, intensity, average path length, betweenness centrality, and clustering coefficient. The static invulnerability of the airspace sector network was evaluated through simulation, and the network efficiency and the size of the connected component were used to assess its invulnerability. A study was conducted in North China based on the ATC sector network. The findings of the study revealed that the sector network did not exhibit the traits of a small-world network model, characterized by short average path lengths and high clustering coefficients. The evaluation of network invulnerability showed that the network’s invulnerability varied depending on the attack strategy used. It was discovered that attacking sectors with high betweenness resulted in the most significant harm to network invulnerability, and betweenness centrality was considered to be a useful indicator for identifying critical sectors that require optimization. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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24 pages, 6810 KiB

Open AccessArticle

A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)

by Chen Deng, Chengqi Cheng, Tengteng Qu, Shuang Li and Bo Chen

Aerospace 2023, 10(3), 217; https://doi.org/10.3390/aerospace10030217 - 24 Feb 2023

Cited by 3 | Viewed by 2189

Abstract

With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level [...] Read more.

With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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24 pages, 6753 KiB

Open AccessArticle

Tactical Conflict Solver Assisting Air Traffic Controllers Using Deep Reinforcement Learning

by Dong Sui, Chenyu Ma and Chunjie Wei

Aerospace 2023, 10(2), 182; https://doi.org/10.3390/aerospace10020182 - 15 Feb 2023

Cited by 3 | Viewed by 2449

Abstract

To assist air traffic controllers (ATCOs) in resolving tactical conflicts, this paper proposes a conflict detection and resolution mechanism for handling continuous traffic flow by adopting finite discrete actions to resolve conflicts. The tactical conflict solver (TCS) was developed based on deep reinforcement [...] Read more.

To assist air traffic controllers (ATCOs) in resolving tactical conflicts, this paper proposes a conflict detection and resolution mechanism for handling continuous traffic flow by adopting finite discrete actions to resolve conflicts. The tactical conflict solver (TCS) was developed based on deep reinforcement learning (DRL) to train a TCS agent with the actor–critic using a Kronecker-factored trust region. The agent’s actions are determined by the ATCOs’ instructions, such as altitude, speed, and heading adjustments. The reward function is designed in accordance with air traffic control regulations. Considering the uncertainty in a real-life situation, this study characterised the deviation of the aircraft’s estimated position to improve the feasibility of conflict resolution schemes. A DRL environment was developed with the actual airspace structure and traffic density of the air traffic operation simulation system. Results show that for 1000 test samples, the trained TCS could resolve 87.1% of the samples. The conflict resolution rate decreased slightly to 81.2% when the airspace density was increased by a factor of 1.4. This research can be applied to intelligent decision-making systems for air traffic control. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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32 pages, 7740 KiB

Open AccessArticle

A Large Neighborhood Search Algorithm with Simulated Annealing and Time Decomposition Strategy for the Aircraft Runway Scheduling Problem

by Jiaming Su, Minghua Hu, Yingli Liu and Jianan Yin

Aerospace 2023, 10(2), 177; https://doi.org/10.3390/aerospace10020177 - 14 Feb 2023

Cited by 3 | Viewed by 2480

Abstract

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) [...] Read more.

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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16 pages, 20966 KiB

Open AccessArticle

Performance Impact Assessment of Reducing Separation Minima for En-Route Operations

by Marta Pérez Maroto, Javier García-Heras, Luis Pérez Sanz, Lidia Serrano-Mira and Javier Alberto Pérez-Castán

Aerospace 2022, 9(12), 772; https://doi.org/10.3390/aerospace9120772 - 29 Nov 2022

Cited by 2 | Viewed by 2309

Abstract

The required minimum separation distance between aircraft is believed to be one of the limiting factors on airspace capacity. In recent decades, aircraft separation rules have been modified by progressively shortening the required minimum separation distance. Following this trend in the coming years, [...] Read more.

The required minimum separation distance between aircraft is believed to be one of the limiting factors on airspace capacity. In recent decades, aircraft separation rules have been modified by progressively shortening the required minimum separation distance. Following this trend in the coming years, a further reduction in the minimum separation distance would be expected. Still, a thorough assessment of the impact of this action on air traffic management performance should be carried out before investing in a reduction of separation minima. A Monte Carlo analysis of the en-route Spanish airspace shows that it is worth reducing the en-route minimum separation distance from 5 NM to 3 NM. This paper shows that a separation minima reduction will bring significant fuel savings, flight delay reduction, air traffic controller workload drop, and overall improvement of safety. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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16 pages, 5827 KiB

Open AccessArticle

Three-Dimensional ANP Evaluation Method Based on Spatial Position Uncertainty under RNP Operation

by Yuting Dai, Jizhou Lai, Bin Zhu, Zhimin Li, Xiao Sun and Pin Lv

Aerospace 2022, 9(11), 703; https://doi.org/10.3390/aerospace9110703 - 10 Nov 2022

Cited by 1 | Viewed by 2497

Abstract

Performance-based navigation (PBN) operations based on the required navigation performance (RNP) operations are the trend for civil aviation in the future. In order to further ensure the safety and efficiency of civil aviation operation, RNP will transition from 2D to 3D/4D. Accurately evaluating [...] Read more.

Performance-based navigation (PBN) operations based on the required navigation performance (RNP) operations are the trend for civil aviation in the future. In order to further ensure the safety and efficiency of civil aviation operation, RNP will transition from 2D to 3D/4D. Accurately evaluating the actual navigation performance in three-dimensional directions (3D ANP) under civil aircraft RNP operation is important to guarantee the safe flight of civil aircraft. However, the traditional two-dimensional ANP evaluation method mainly focuses on plane navigation performance along the track, lacks the evaluation of the vertical direction. Moreover, the traditional three-dimensional accuracy evaluation method is based on the assumption of three-dimensional independence and uniformity, which can only carry out approximate calculation, and the evaluation result is inaccurate. Therefore, this paper constructs a three-dimensional ellipsoid error probability (EEP) evaluation model for the spatial position uncertainty of the navigation output of the flight management system in three-dimensional directions, and gives the three-dimensional accurate ANP calculation results through iterative numerical integration. The simulation results show that the evaluation method proposed in this paper can accurately evaluate the actual navigation performance of the airborne navigation system in all directions of 3D space, and has higher evaluation accuracy and precision than the traditional ANP evaluation method, which is of great significance to ensure the flight safety of civil aircraft under 3D/4D RNP operation in the future. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 3084 KiB

Open AccessArticle

Controlling Aircraft Inter-Arrival Time to Reduce Arrival Traffic Delay via a Queue-Based Integer Programming Approach

by Koki Higasa and Eri Itoh

Aerospace 2022, 9(11), 663; https://doi.org/10.3390/aerospace9110663 - 28 Oct 2022

Cited by 9 | Viewed by 2555

Abstract

Despite the importance of controlling the inter-arrival times of flights to propose strategies for efficient arrival management by the Arrival Manager (AMAN), the specific guidelines of such adjustments and their effect on reducing delays have not been explicitly considered. Accordingly, this paper proposes [...] Read more.

Despite the importance of controlling the inter-arrival times of flights to propose strategies for efficient arrival management by the Arrival Manager (AMAN), the specific guidelines of such adjustments and their effect on reducing delays have not been explicitly considered. Accordingly, this paper proposes a novel approach, which integrates the

G_{t} / G I / s_{t} + G I

time-varying fluid model and nonlinear integer programming to flatten the arrival rate at terminal gates. This, in turn, is achieved by minimizing the variance in inter-arrival times by penalizing any excessive change in arrival time, considering operational constraints. The results for Tokyo International Airport show potential to significantly reduce arrival traffic delays by minimizing said variance. This study may also spawn subsequent work, which builds a queuing network comprising upstream and terminal airspace and demonstrates the scope to reduce delays in the terminal airspace by controlling inter-arrival times at the upstream airspace. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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18 pages, 1366 KiB

Open AccessArticle

A Policy-Reuse Algorithm Based on Destination Position Prediction for Aircraft Guidance Using Deep Reinforcement Learning

by Zhuang Wang, Yi Ai, Qinghai Zuo, Shaowu Zhou and Hui Li

Aerospace 2022, 9(11), 632; https://doi.org/10.3390/aerospace9110632 - 22 Oct 2022

Cited by 4 | Viewed by 1701

Abstract

Artificial intelligence for aircraft guidance is a hot research topic, and deep reinforcement learning is one of the promising methods. However, due to the different movement patterns of destinations in different guidance tasks, it is inefficient to train agents from scratch. In this [...] Read more.

Artificial intelligence for aircraft guidance is a hot research topic, and deep reinforcement learning is one of the promising methods. However, due to the different movement patterns of destinations in different guidance tasks, it is inefficient to train agents from scratch. In this article, a policy-reuse algorithm based on destination position prediction is proposed to solve this problem. First, the reward function is optimized to improve flight trajectory quality and training efficiency. Then, by predicting the possible termination position of the destinations in different moving patterns, the problem is transformed into a fixed-position destination aircraft guidance problem. Last, taking the agent in the fixed-position destination scenario as the baseline agent, a new guidance agent can be trained efficiently. Simulation results show that this method can significantly improve the training efficiency of agents in new tasks, and its performance is stable in tasks with different similarities. This research broadens the application scope of the policy-reuse approach and also enlightens the research in other fields. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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19 pages, 862 KiB

Open AccessArticle

Image-Based Multi-Agent Reinforcement Learning for Demand–Capacity Balancing

by Sergi Mas-Pujol, Esther Salamí and Enric Pastor

Aerospace 2022, 9(10), 599; https://doi.org/10.3390/aerospace9100599 - 14 Oct 2022

Cited by 2 | Viewed by 1952

Abstract

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines [...] Read more.

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines operations. Thus, the central flow management unit continually seeks to improve traffic flow management to reduce delays and congestion. In this work, we investigated the use of reinforcement learning (RL) methods to compute policies to solve demand–capacity imbalances (a.k.a. congestion) during the pre-tactical phase. To address cases where the expected demands exceed the airspace sector capacity, we considered agents representing flights who have to decide on ground delays jointly. To overcome scalability issues, we propose using raw pixel images as input, which can represent an arbitrary number of agents without changing the system’s architecture. This article compares deep Q-learning and deep deterministic policy gradient algorithms with different configurations. Experimental results, using real-world data for training and validation, confirm the effectiveness of our approach to resolving demand–capacity balancing problems, showing the robustness of the RL approach presented in this article. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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15 pages, 2348 KiB

Open AccessArticle

Dynamic Prediction of Air Traffic Situation in Large-Scale Airspace

by Dong Sui, Kechen Liu and Qian Li

Aerospace 2022, 9(10), 568; https://doi.org/10.3390/aerospace9100568 - 29 Sep 2022

Cited by 3 | Viewed by 2669

Abstract

Air traffic situation prediction is critical for traffic flow management and the optimal allocation of airspace resources. In this study, the multi-sector airspace scenario is abstracted into an undirected graph. A spatiotemporal graph convolutional network (STGCN) model is developed to portray the spatiotemporal [...] Read more.

Air traffic situation prediction is critical for traffic flow management and the optimal allocation of airspace resources. In this study, the multi-sector airspace scenario is abstracted into an undirected graph. A spatiotemporal graph convolutional network (STGCN) model is developed to portray the spatiotemporal correlation between the sector operational situation changes. The model can predict multi-sector operational situations using time series data such as sector operational situation data and traffic volume within the sector. Experimenting on the air traffic situation dataset of 30 area sectors in the Shanghai control area revealed that the STGCN model has a prediction accuracy of above 90%, and it outperforms the benchmark method of traditional traffic prediction. This proves the effectiveness of the proposed situation prediction model. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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17 pages, 1782 KiB

Open AccessArticle

Assessment of Potential Conflict Detection by the ATCo

by Raquel Delgado-Aguilera Jurado, Víctor Fernando Gómez Comendador, María Zamarreño Suárez, Francisco Pérez Moreno, Christian Eduardo Verdonk Gallego and Rosa María Arnaldo Valdés

Aerospace 2022, 9(9), 522; https://doi.org/10.3390/aerospace9090522 - 17 Sep 2022

Cited by 2 | Viewed by 2169

Abstract

The main goal of this article is to analyse the probability of detecting potential conflicts by the Air Traffic Controller (ATCo). The ATCo ensures the safety of aircraft and one of its main functions is collision avoidance. Collision avoidance is known as separation [...] Read more.

The main goal of this article is to analyse the probability of detecting potential conflicts by the Air Traffic Controller (ATCo). The ATCo ensures the safety of aircraft and one of its main functions is collision avoidance. Collision avoidance is known as separation provision and this term means assuring the safe distance between each aircraft by sides, vertical and longitudinal minimums of separation. The air traffic controller must ensure a high level of airspace capacity. The work performance is related to high demands on individual characteristics, knowledge, skills and, of course, air traffic characteristics. In addition to analysing the probability of detecting potential conflicts, the study of the most influential factors on this safety event is considered of special relevance since the ATCo represents the last executive section of the air traffic control system and failure to detect potential conflicts could lead to a possible infringement of the minimum separation distances between aircraft or even a collision. In order to carry out this approach, Bayesian Networks will be used due to their high predictive capacity. In addition, a dual approach based on knowledge and real operational data provided by an ANSP will be used. These data are one of the great advantages of this study compared to those included in the current literature. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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21 pages, 4177 KiB

Open AccessArticle

Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation

by Lidia Serrano-Mira, Marta Pérez Maroto, Eduardo S. Ayra, Javier Alberto Pérez-Castán, Schon Z. Y. Liang-Cheng, Víctor Gordo Arias and Luis Pérez-Sanz

Aerospace 2022, 9(9), 513; https://doi.org/10.3390/aerospace9090513 - 15 Sep 2022

Cited by 3 | Viewed by 2585

Abstract

A Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation [...] Read more.

A Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation infringement. This study develops a model to identify the factors contributing to a LOS between aircraft pairs. A Bayesian Network (BN) model is used to estimate the conditional dependencies of the factors affecting criticality, that is, how close the LOS has come to becoming a collision. This probabilistic model is built using GeNIe software from data (based on a database created from incident analysis) and expert judgment. The results of the model allow identification of how factors related to the scenario, the human factor (ATC and flight crew) or the technical systems, affect the criticality of the LOS. Based on this information, it is possible to exclude irrelevant elements that do not contribute or whose influence could be neglected, and to prioritize work on the most important ones, in order to increase ATM safety. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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22 pages, 1964 KiB

Open AccessArticle

Distributed Conflict Resolution at High Traffic Densities with Reinforcement Learning

by Marta Ribeiro, Joost Ellerbroek and Jacco Hoekstra

Aerospace 2022, 9(9), 472; https://doi.org/10.3390/aerospace9090472 - 25 Aug 2022

Cited by 7 | Viewed by 2307

Abstract

Future operations involving drones are expected to result in traffic densities that are orders of magnitude higher than any observed in manned aviation. Current geometric conflict resolution (CR) methods have proven to be very efficient at relatively moderate densities. However, at higher densities, [...] Read more.

Future operations involving drones are expected to result in traffic densities that are orders of magnitude higher than any observed in manned aviation. Current geometric conflict resolution (CR) methods have proven to be very efficient at relatively moderate densities. However, at higher densities, performance is hindered by the unpredictable emergent behaviour from neighbouring aircraft. Reinforcement learning (RL) techniques are often capable of identifying emerging patterns through training in the environment. Although some work has started introducing RL to resolve conflicts and ensure separation between aircraft, it is not clear how to employ these methods with a higher number of aircraft, and whether these can compare to or even surpass the performance of current CR geometric methods. In this work, we employ an RL method for distributed conflict resolution; the method is completely responsible for guaranteeing minimum separation of all aircraft during operation. Two different action formulations are tested: (1) where the RL method controls heading, and speed variation; (2) where the RL method controls heading, speed, and altitude variation. The final safety values are directly compared to a state-of-the-art distributed CR algorithm, the Modified Voltage Potential (MVP) method. Although, overall, the RL method is not as efficient as MVP in reducing the total number of losses of minimum separation, its actions help identify favourable patterns to avoid conflicts. The RL method has a more preventive behaviour, defending in advance against nearby neighbouring aircraft not yet in conflict, and head-on conflicts while intruders are still far away. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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21 pages, 740 KiB

Open AccessArticle

Study of the Impact of Traffic Flows on the ATC Actions

by Guillermo Gutiérrez Teuler, Rosa María Arnaldo Valdés, Victor Fernando Gómez Comendador, Patricia María López de Frutos and Rubén Rodríguez Rodríguez

Aerospace 2022, 9(8), 467; https://doi.org/10.3390/aerospace9080467 - 22 Aug 2022

Cited by 3 | Viewed by 1843

Abstract

It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to [...] Read more.

It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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15 pages, 1963 KiB

Open AccessArticle

Natural Language Processing of Aviation Safety Reports to Identify Inefficient Operational Patterns

by Ayaka Miyamoto, Mayank V. Bendarkar and Dimitri N. Mavris

Aerospace 2022, 9(8), 450; https://doi.org/10.3390/aerospace9080450 - 17 Aug 2022

Cited by 17 | Viewed by 3696

Abstract

With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational improvements that employ existing aircraft [...] Read more.

With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational improvements that employ existing aircraft and require no new infrastructure are fit for this goal. While quantified data collected throughout aviation, such as arrival/departure statistics and flight data, have been well-utilized, text data collected through safety reports have not been leveraged to their full extent. In this paper, a methodology is presented that can use aviation text data to identify high-level causes of flight delays and cancellations, using delays as a metric of operational inefficiency. The dataset is extracted from the Aviation Safety Reporting System (ASRS), which includes voluntary safety incident reports in text narrative and metadata formats. The methodology uses natural language processing tools, K Means clustering, and dimensionality reduction by t-Distributed Stochastic Neighbor Embedding (t-SNE) to categorize and visualize narratives. The method identified 7 major clusters and a total of 23 sub-clusters. A comparison between the subclusters’ topics and the causes of flight delays revealed by the quantified data shows that the ASRS database provides a unique safety perspective to delay cause identification, as illustrated by the method’s identification of maintenance as the main cause of delays, rather than weather. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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19 pages, 13950 KiB

Open AccessArticle

Research on Game Theory of Air Traffic Management Cyber Physical System Security

by Zhijun Wu, Ruochen Dong and Peng Wang

Aerospace 2022, 9(8), 397; https://doi.org/10.3390/aerospace9080397 - 23 Jul 2022

Cited by 3 | Viewed by 2825

Abstract

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if [...] Read more.

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if the two parties have different goals. The offensive and defensive game process of the air traffic management cyber physical system is a non-cooperative and incomplete information dynamic game. The attacker can choose to camouflage the type of attack launched. The attack detection device configured in the system has a certain probability that the attack type can be successfully detected. According to the type of attack detected, the defender updates the posterior belief of the attack type and selects the corresponding protective strategies. According to the game process of offense and defense, a dynamic Bayesian game model of the air traffic management cyber physical system is established, the possible perfect Bayesian Nash equilibrium and its existence conditions are solved, and a complete mathematical model is constructed. The analysis shows that the dynamic Bayesian game model of the air traffic management cyber physical system can help the system defender to quickly obtain an equilibrium strategy and reduce the loss of the system as much as possible. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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20 pages, 3580 KiB

Open AccessArticle

Looking into the Crystal Ball—How Automated Fast-Time Simulation Can Support Probabilistic Airport Management Decisions

by Oliver Pohling, Sebastian Schier-Morgenthal and Sandro Lorenz

Aerospace 2022, 9(7), 389; https://doi.org/10.3390/aerospace9070389 - 19 Jul 2022

Viewed by 2298

Abstract

Airport management plays a key role in the air traffic system. Introducing resources at the right time can minimize the effects of disruptions, reduce delays, and save costs as well as optimize the carbon footprint of the airport. Efficient decision-making is a challenge [...] Read more.

Airport management plays a key role in the air traffic system. Introducing resources at the right time can minimize the effects of disruptions, reduce delays, and save costs as well as optimize the carbon footprint of the airport. Efficient decision-making is a challenge due to the uncertainty of the upcoming events and the results of the applied countermeasures. So-called ‘what-if’ systems are under research to support the decision-makers. These systems consist of a user interface, a case management system, and a prediction engine. Within this paper, we evaluate different types of prediction engines (flow, event, and motion models) that can be used for airport management what-if systems by comparing them in terms of accuracy and calculation speed. Hence, two different operational situations are examined to evaluate the performance of the prediction engines. The comparison shows that accuracy and calculation speed are opposed. The flow model has the lowest accuracy but the shortest calculation time and the motion model has the highest accuracy but the longest calculation time. The event model lies between the other two models. The acceptable accuracy of a prediction tool is strongly dependent on the respective airport, whereas the calculation time is strongly dependent on the available decision time. Regarding airport management, this means that the selection of a prediction engine has to be made in dependence of the airport and the decision processes. The results show the advantages and disadvantages of each prediction engine and provide a first quantification by which a selection for what-if systems can happen. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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17 pages, 957 KiB

Open AccessArticle

On the Estimation of Vector Wind Profiles Using Aircraft-Derived Data and Gaussian Process Regression

by Marius Marinescu, Alberto Olivares, Ernesto Staffetti and Junzi Sun

Aerospace 2022, 9(7), 377; https://doi.org/10.3390/aerospace9070377 - 13 Jul 2022

Cited by 4 | Viewed by 2392

Abstract

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on the Adolfo Suarez [...] Read more.

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on the Adolfo Suarez Madrid-Barajas airport. A continuous nowcast of the wind profile is performed in which wind observations are derived from the aircraft states and assimilated into the model. It is well known that wind is one of the utmost contributors to uncertainties in the current and future paradigm of Air Traffic Management. Accurate wind information is key in continuous climb and descent operations, spacing, four dimensional trajectory-based operations, and aircraft performance studies, among others. In this work, wind data are obtained indirectly from the aircraft’s states broadcast by the Mode S and ADS-B aircraft surveillance systems. The Gaussian process regression is adapted to this framework and used to solve the problem. The presented method allows to construct a complete vector wind profile at any specific position that is continuous in time and altitude; namely, there is no need for grid points and time discretisation. The Gaussian process regression is a very flexible estimator which is statistically consistent under general conditions, meaning that it converges to the underground truth when more and more data are dispensed. In addition, the Gaussian process regression approach provides the whole probability distribution of any particular estimation, allowing confidence intervals to be computed naturally. In the case study presented in this paper, in which the wind is constantly estimated, the Gaussian process regression model is iteratively updated every 15 min to capture possible changes in the wind behaviour and give an estimation of the wind profile every half a minute. The method has been validated using a test dataset, achieving a reduction of 50% of the prediction uncertainty in comparison to a baseline model. Moreover, two popular wind profile estimators based on the Kalman filter are also implemented for the sake of comparison. The Kalman filter outperforms the baseline model, but it does not outperform the Gaussian process regression with errors higher by around 35%, in comparison. The obtained results show that the Gaussian process regression of aircraft-derived data reliably nowcast the wind state, which is key in Air Traffic Management. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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Review

Jump to: Research

30 pages, 11096 KiB

Open AccessReview

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

by Euclides Carlos Pinto Neto, Derick Moreira Baum, Jorge Rady de Almeida, Jr., João Batista Camargo, Jr. and Paulo Sergio Cugnasca

Aerospace 2023, 10(4), 358; https://doi.org/10.3390/aerospace10040358 - 4 Apr 2023

Cited by 11 | Viewed by 7568

Abstract

Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in [...] Read more.

Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in several domains, and Deep Learning (DL) has attracted attention due to its impressive results and disruptive capabilities. The adoption of DL models in ATM solutions enables new cognitive services that have never been considered before. The main goal of this research is to present a comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM). This review focuses on describing applications, identifying opportunities, and highlighting open challenges to foster the evolution of ATM systems. To accomplish this, we discuss the fundamental topics of DL and ATM and categorize the contributions based on different approaches. First, works are grouped based on the DL approach adopted. Then, future directions are identified based on the ATM solution area. Finally, open challenges are listed for both DL applications and ATM solutions. This article aims to support the community by identifying research problems to be faced in the future. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management)

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