Aerospace

Research

18 pages, 2550 KiB

Open AccessArticle

Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs

by George Vouros, Ioannis Ioannidis, Georgios Santipantakis, Theodore Tranos, Konstantinos Blekas, Marc Melgosa and Xavier Prats

Aerospace 2024, 11(11), 937; https://doi.org/10.3390/aerospace11110937 - 12 Nov 2024

Viewed by 418

Abstract

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, [...] Read more.

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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33 pages, 9602 KiB

Open AccessArticle

Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques

by Daniel Amigo, David Sánchez Pedroche, Jesús García, José Manuel Molina, Jekaterina Trofimova, Emmanuel Voet and Benoît Van Bogaert

Aerospace 2024, 11(11), 900; https://doi.org/10.3390/aerospace11110900 - 31 Oct 2024

Viewed by 364

Abstract

This paper presents significant enhancements to the vertical reconstruction component of EUROCONTROL’s Surveillance Analysis Support System for ATC Centres (SASS-C). We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved invalid height detection process [...] Read more.

This paper presents significant enhancements to the vertical reconstruction component of EUROCONTROL’s Surveillance Analysis Support System for ATC Centres (SASS-C). We introduce four key improvements: (1) a novel segmentation algorithm for more precise flight phase identification, (2) an improved invalid height detection process using LOWESS and sliding window analysis, (3) a protection mechanism against simultaneous measurements at the Kalman filter level, and (4) an optimized approach for smooth overshoot correction during segment transitions. These advancements address limitations in the current system, particularly in trajectory segmentation accuracy and robustness against measurement anomalies. Our methodology employs both synthetic and real-world data for comprehensive evaluation, ensuring performance under controlled and operational conditions. The results demonstrate substantial improvements in segmentation precision, outlier detection, and overall trajectory reconstruction quality. The invalid detection algorithm, while incurring a slight computational cost, significantly enhances trajectory accuracy. These enhancements contribute to more reliable air traffic analysis, supporting safer and more efficient airspace management. The paper concludes by discussing potential future work, including the application of machine learning techniques and the extension of these improvements to horizontal reconstruction processes. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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21 pages, 4962 KiB

Open AccessArticle

Measurement of Driving Conditions of Aircraft Ground Support Equipment at Tokyo International Airport

by Yuka Kuroda, Satoshi Sato and Shinya Hanaoka

Aerospace 2024, 11(11), 873; https://doi.org/10.3390/aerospace11110873 - 24 Oct 2024

Viewed by 1165

Abstract

With the global increase in air transport demand, the shortage of ground handling personnel to support ground operations at airports has become a major challenge, impacting airport services and causing considerable flight delays. This study presents a novel method to generate trip data [...] Read more.

With the global increase in air transport demand, the shortage of ground handling personnel to support ground operations at airports has become a major challenge, impacting airport services and causing considerable flight delays. This study presents a novel method to generate trip data that specify the origin and destination locations as the purpose of travel for each ground support equipment (GSE) vehicle. The proposed method uses data obtained from comprehensive observations of 2234 GSE vehicles over a 24 h × 7 d time interval at Tokyo International Airport. From these observations and trip data, the characteristics of the driving conditions for each GSE vehicle type, the locations where GSE traffic volume increases in the airport, and changes in the time interval are identified. The primary results show that the GSE traffic volume is the highest mainly around passenger terminals and in the vehicle corridors connecting these terminals, which aligns with the airport’s operational status. Investigating GSE driving conditions, such as the traffic flow throughout an airport, can provide valuable data to improve the efficiency of GSE scheduling and facilitate the introduction of automated driving technology. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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15 pages, 4335 KiB

Open AccessArticle

Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks

by Leilei Deng, Weijun Pan, Tian Luan, Chen Zhang and Yuanfei Leng

Aerospace 2024, 11(10), 840; https://doi.org/10.3390/aerospace11100840 - 11 Oct 2024

Viewed by 709

Abstract

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing [...] Read more.

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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13 pages, 9449 KiB

Open AccessArticle

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

by Jianwei Gao and Weijun Pan

Aerospace 2024, 11(9), 704; https://doi.org/10.3390/aerospace11090704 - 28 Aug 2024

Viewed by 1494

Abstract

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, [...] Read more.

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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17 pages, 2839 KiB

Open AccessArticle

Bidirectional Long Short-Term Memory Development for Aircraft Trajectory Prediction Applications to the UAS-S4 Ehécatl

by Seyed Mohammad Hashemi, Ruxandra Mihaela Botez and Georges Ghazi

Aerospace 2024, 11(8), 625; https://doi.org/10.3390/aerospace11080625 - 31 Jul 2024

Viewed by 909

Abstract

The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This [...] Read more.

The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This paper represents a novel data-driven trajectory prediction methodology based on applying the Long Short-Term Memory (LSTM) prediction algorithm to the UAS-S4 Ehécatl. An LSTM model was designed as the baseline and then developed into a Staked LSTM to better capture complex and hierarchical temporal trajectory patterns. Next, the Bidirectional LSTM was developed for a better understanding of the contextual trajectories from both its past and future data points, and to provide a more comprehensive temporal perspective that could enhance its accuracy. LSTM-based models were evaluated in terms of mean absolute percentage errors. The results reveal the superiority of the Bidirectional LSTM, as it could predict UAS-S4 trajectories more accurately than the Stacked LSTM. Moreover, the developed Bidirectional LSTM was compared with other state-of-the-art deep neural networks aimed at aircraft trajectory prediction. Promising results confirmed that Bidirectional LSTM exhibits the most stable MAPE across all prediction horizons. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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19 pages, 3360 KiB

Open AccessArticle

ATC-SD Net: Radiotelephone Communications Speaker Diarization Network

by Weijun Pan, Yidi Wang, Yumei Zhang and Boyuan Han

Aerospace 2024, 11(7), 599; https://doi.org/10.3390/aerospace11070599 - 22 Jul 2024

Viewed by 1150

Abstract

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules [...] Read more.

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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13 pages, 2308 KiB

Open AccessArticle

Characteristics of Ice Super Saturated Regions in Washington, D.C. Airspace (2019–2023)

by Kayla Ebright and Lance Sherry

Aerospace 2024, 11(7), 587; https://doi.org/10.3390/aerospace11070587 - 17 Jul 2024

Viewed by 969

Abstract

Contrails are estimated to contribute 2% of the Earth’s anthropogenic global warming. Contrails are ice crystal clouds formed by the emission of soot and water vapor from jet engines in atmospheric conditions known as Ice Super Saturated (ISS) regions. The formation of contrails [...] Read more.

Contrails are estimated to contribute 2% of the Earth’s anthropogenic global warming. Contrails are ice crystal clouds formed by the emission of soot and water vapor from jet engines in atmospheric conditions known as Ice Super Saturated (ISS) regions. The formation of contrails can be avoided by flying over or under the ISS regions. Aircraft operators/dispatchers and air traffic control need to know the location of ISS regions in a given airspace to flightplan to avoid contrails. This paper describes the statistics for the presence of ISS regions in the airspace over metropolitan Washington, D.C. These statistics can be used to better understand the operational implications for contrail avoidance. Based on the measurements taken from the twice-daily launch of an aerosonde from Sterling, Virginia (adjacent to Washington, D.C.), analysis of five years of data (2019–2023) indicated that this airspace experiences ISS regions 40% of the days. ISS regions were equally likely during daylight hours (26%) than nighttime (27%). The vertical depth of the ISS region averaged 3000 feet but with a median of 2000 feet. The ISS region floor and ceiling varied by season, with an annual average floor of FL330 and ceiling of FL360. The implications of these results on the operations to avoid contrails, limitations, and future work are discussed. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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18 pages, 6815 KiB

Open AccessArticle

Air Traffic Control Speech Enhancement Method Based on Improved DNN-IRM

by Yuezhou Wu, Pengfei Li and Siling Zhang

Aerospace 2024, 11(7), 581; https://doi.org/10.3390/aerospace11070581 - 16 Jul 2024

Viewed by 1093

Abstract

The quality of air traffic control speech is crucial. However, internal and external noise can impact air traffic control speech quality. Clear speech instructions and feedback help optimize flight processes and responses to emergencies. The traditional speech enhancement method based on a deep [...] Read more.

The quality of air traffic control speech is crucial. However, internal and external noise can impact air traffic control speech quality. Clear speech instructions and feedback help optimize flight processes and responses to emergencies. The traditional speech enhancement method based on a deep neural network and ideal ratio mask (DNN-IRM) is prone to distortion of the target speech in a strong noise environment. This paper introduces an air traffic control speech enhancement method based on an improved DNN-IRM. It employs LeakyReLU as an activation function to alleviate the gradient vanishing problem, improves the DNN network structure to enhance the IRM estimation capability, and adjusts the IRM weights to reduce noise interference in the target speech. The experimental results show that, compared with other methods, this method improves the perceptual evaluation of speech quality (PESQ), short-term objective intelligibility (STOI), scale-invariant signal-to-noise ratio (SI-SNR), and speech spectrogram clarity. In addition, we use this method to enhance real air traffic control speech, and the speech quality is also improved. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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25 pages, 11665 KiB

Open AccessArticle

Identification of Traffic Flow Spatio-Temporal Patterns and Their Associated Weather Factors: A Case Study in the Terminal Airspace of Hong Kong

by Weining Zhang, Weijun Pan, Xinping Zhu, Changqi Yang, Jinghan Du and Jianan Yin

Aerospace 2024, 11(7), 531; https://doi.org/10.3390/aerospace11070531 - 28 Jun 2024

Cited by 2 | Viewed by 823

Abstract

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, [...] Read more.

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, and association rule mining. To fully characterize trajectory structure, abnormal trajectories and typical operations are sequentially extracted based on a deep autoencoder network with two specially designed loss functions. Then, using these extracted elements as basic components to further construct and cluster per-hour-level descriptions of airspace structure, the spatio-temporal patterns of air traffic flow can be recognized. Finally, the association rule mining technique is applied to find sets of weather factors that often appear together with each flow pattern. Experimental analysis is demonstrated on two months of arrival flight trajectories at Hong Kong International Airport (HKIA). The results clearly show that the proposed framework effectively captures spatial anomalies, fine-grained trajectory structures, and representative flow patterns. More importantly, it also reveals that those flow patterns with non-conforming behaviors result from complex interactions of various weather factors. The findings provide valuable insights into the causal relationships between weather factors and changes in flow patterns, greatly enhancing the situational awareness of TMA. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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17 pages, 16204 KiB

Open AccessArticle

Activity Modeling and Characterization for Airport Bird Situation Awareness Using Avian Radar Datasets

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

Aerospace 2024, 11(6), 442; https://doi.org/10.3390/aerospace11060442 - 30 May 2024

Viewed by 908

Abstract

Birds in airport airspaces are critical threats to aviation safety. Avian radar systems are effective for long-range bird monitoring and hazard warning, but their functionalities are confined to a short-term temporal scale. Spatial–temporal activity modeling and characterization for birds are not studied comprehensively [...] Read more.

Birds in airport airspaces are critical threats to aviation safety. Avian radar systems are effective for long-range bird monitoring and hazard warning, but their functionalities are confined to a short-term temporal scale. Spatial–temporal activity modeling and characterization for birds are not studied comprehensively from historical radar datasets. This paper proposes a radar data analysis framework to characterize bird activities as a long-term functionality complement. Spatial domain modeling initializes data mining by extracting reference spots for data filtering. Bird activities are quantified in the temporal domain. Activity degrees are utilized for periodicity extraction with the daily segment random permutation strategy. Categorical probabilities are calculated to interpret bird activity periodicity characters. Historical radar datasets collected from an avian radar system are adopted for validation. The extracted activity periodicity trends for diurnal birds present prominent consistency with artificial observation records. Migratory bird periodicity trends present a good match with ornithology understandings. A preliminary experiment is presented to indicate the possibility of predicting bird activity levels, especially for migratory birds. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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14 pages, 30993 KiB

Open AccessArticle

Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks

by Miguel Ángel de Frutos Carro, Carlos Cerdán Villalonga and Antonio Barrientos Cruz

Aerospace 2024, 11(5), 383; https://doi.org/10.3390/aerospace11050383 - 10 May 2024

Cited by 1 | Viewed by 1575

Abstract

Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of [...] Read more.

Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of determining whether simulated data can train and validate navigation operations in complex airport environments. The methodology includes a comparative analysis employing image processing techniques and object detection algorithms. A comparative analysis of two different datasets was conducted: a synthetic dataset that mirrors real airport scenarios, generated using the Microsoft Flight Simulator 2020®video game, and a real-world dataset. The results indicate that models trained on a combination of both real and synthetic images perform much better in terms of adaptability and accuracy compared to those trained only on one type of dataset. This analysis makes a significant contribution to the field of autonomous airport navigation and offers a cost-effective and practical solution to overcome the challenges of dataset acquisition and algorithm validation. It is thus believed that this study lays the groundwork for future advancements in the field. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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16 pages, 1701 KiB

Open AccessArticle

A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method

by Yunfang Peng, Xuechun Hu and Beixin Xia

Aerospace 2024, 11(5), 378; https://doi.org/10.3390/aerospace11050378 - 9 May 2024

Viewed by 924

Abstract

Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into [...] Read more.

Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into a master problem and sub-problems. The algorithm uses MIP in the master problem to determine flight cancellations and aircraft replacements. In the sub-problems, MIP or CP is used to determine the departure time of delayed flights. Later, incorporating sectional constraints into the main problem and iterating until an optimal solution is obtained. Furthermore, the added cutting plane constraint in the iterations of the Benders decomposition algorithm are strengthened to eliminate more inferior solutions. By comparing the results of CPLEX, the Logic-Based Benders decomposition algorithm, and the enhanced Benders decomposition algorithm, it is verified that the improved Benders decomposition algorithm can solve large-scale examples more efficiently with a faster time and fewer iterations. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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17 pages, 2019 KiB

Open AccessArticle

Exploring the Impact of Pandemic Measures on Airport Performance

by James J. H. Liou, Chih Wei Chien, Pedro Jose Gudiel Pineda, Chun-Sheng Joseph Li and Chao-Che Hsu

Aerospace 2024, 11(5), 373; https://doi.org/10.3390/aerospace11050373 - 8 May 2024

Cited by 1 | Viewed by 1418

Abstract

The impact of COVID-19 measures on airport performance is obvious, and there have been numerous studies on this topic. However, most of these studies discuss prevention measures, the effects on airport operations, forecasts of economic impacts, changes in service quality, etc. There is [...] Read more.

The impact of COVID-19 measures on airport performance is obvious, and there have been numerous studies on this topic. However, most of these studies discuss prevention measures, the effects on airport operations, forecasts of economic impacts, changes in service quality, etc. There is a lack of research on the effects of various prevention measures on airport operations and the interrelationships between these measures. This study focuses on addressing this gap. In this study, an integrated approach is devised that combines the decision-making trial and evaluation laboratory (DEMATEL) method and interpretive structural modeling (ISM). This integrated method is useful for exploring the relationship between pandemic measures and airport performance as well as the complex relationship between them, and the combination of methods improves upon the shortcomings of the original models. This study reveals that mandating vaccination certificates for entry into a country is the most significant measure affecting airport performance. Additionally, aircraft movement at the airport has the greatest overall impact and can be considered the most crucial factor influencing airport performance from an operational standpoint. The findings show that both factors directly influence financial performance, as reflected in the net income. Some management implications are provided to mitigate the consequences of the measures taken to counter the pandemic crisis. This integrated approach should also assist authorities and policy-makers in planning cautious action for future crises. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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18 pages, 1268 KiB

Open AccessArticle

Defining Terminal Airspace Air Traffic Complexity Indicators Based on Air Traffic Controller Tasks

by Tea Jurinić, Biljana Juričić, Bruno Antulov-Fantulin and Kristina Samardžić

Aerospace 2024, 11(5), 367; https://doi.org/10.3390/aerospace11050367 - 6 May 2024

Viewed by 1059

Abstract

This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed [...] Read more.

This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed which assumes that terminal complexity could be determined based on approach air traffic controller (ATCO) tasks. The comprehensive list of general approach ATCO tasks was defined using a literature review and observation of training exercises, forming the basis for subsequent expert group workshops which enabled the acquisition of ATCOs’ knowledge data. Through these workshops, new approach ATCO tasks were additionally identified, and terminal complexity indicators were defined with airspace and traffic parameters. These new tasks and indicators present a novelty in this field of research since they incorporate ATCOs’ knowledge as the data input and consider various traffic scenarios, all types of traffic, weather conditions, and off-nominal situations. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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21 pages, 4615 KiB

Open AccessArticle

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

by Lan Ma, Xianran Meng and Zhijun Wu

Aerospace 2024, 11(4), 313; https://doi.org/10.3390/aerospace11040313 - 17 Apr 2024

Cited by 4 | Viewed by 2442

Abstract

With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy terminal airspace, proposing a data-driven 4D trajectory prediction model. Initially, we propose [...] Read more.

With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy terminal airspace, proposing a data-driven 4D trajectory prediction model. Initially, we propose a Spatial Gap Fill (Spat Fill) method to reconstruct each aircraft’s trajectory, resulting in a consistent time interval, noise-free, high-quality trajectory dataset. Subsequently, we design a hybrid neural network based on the seq2seq model, named Attention-TCN-GRU. This consists of an encoding section for extracting features from the data of historical trajectories, an attention module for obtaining the multilevel periodicity in the flight history trajectories, and a decoding section for recursively generating the predicted trajectory sequences, using the output of the coding part as the initial input. The proposed model can effectively capture long-term and short-term dependencies and repetitiveness between trajectories, enhancing the accuracy of 4D trajectory predictions. We utilize a real ADS-B trajectory dataset from the airspace of a busy terminal for validation. The experimental results indicate that the data-driven 4D trajectory prediction model introduced in this study achieves higher predictive accuracy, outperforming some of the current data-driven trajectory prediction methods. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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24 pages, 7240 KiB

Open AccessArticle

Predicting Air Traffic Congestion under Uncertain Adverse Weather

by Juan Nunez-Portillo, Alfonso Valenzuela, Antonio Franco and Damián Rivas

Aerospace 2024, 11(3), 240; https://doi.org/10.3390/aerospace11030240 - 19 Mar 2024

Viewed by 1464

Abstract

This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources [...] Read more.

This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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17 pages, 10405 KiB

Open AccessArticle

Research on Passenger Evacuation Behavior in Civil Aircraft Demonstration Experiments Based on Neural Networks and Modeling

by Zhenyu Feng, Qianqian You, Kun Chen, Houjin Song and Haoxuan Peng

Aerospace 2024, 11(3), 221; https://doi.org/10.3390/aerospace11030221 - 12 Mar 2024

Viewed by 1314

Abstract

Evacuation simulation is an important method for studying and evaluating the safety of passenger evacuation, and the key lies in whether it can accurately predict personnel evacuation behavior in different environments. The existing models have good adaptability in building environments but have weaker [...] Read more.

Evacuation simulation is an important method for studying and evaluating the safety of passenger evacuation, and the key lies in whether it can accurately predict personnel evacuation behavior in different environments. The existing models have good adaptability in building environments but have weaker adaptability to personnel evacuation in civil aircraft cabins with more obstacles and stronger hindrances. We target the narrow seat aisle environment on airplanes and use a BP neural network to establish a continuous displacement model for personnel evacuation. We compare the simulation accuracy of evacuation time with the social force model based on continuous displacement and further compare the similarity of personnel evacuation process behavior. The results show that both models were close to the experimental values in simulating evacuation time, while our BP neural network evacuation model based on experimental data was more accurate in predicting the personnel evacuation process, showing more realistic details such as the probability of conflicts and bottleneck evolution in the cross aisle. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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13 pages, 1826 KiB

Open AccessArticle

Customization of the ASR System for ATC Speech with Improved Fusion

by Jiahao Fan and Weijun Pan

Aerospace 2024, 11(3), 219; https://doi.org/10.3390/aerospace11030219 - 12 Mar 2024

Viewed by 1297

Abstract

In recent years, automatic speech recognition (ASR) technology has improved significantly. However, the training process for an ASR model is complex, involving large amounts of data and a large number of algorithms. The task of training a new model for air traffic control [...] Read more.

In recent years, automatic speech recognition (ASR) technology has improved significantly. However, the training process for an ASR model is complex, involving large amounts of data and a large number of algorithms. The task of training a new model for air traffic control (ATC) is considerable, as it may require many researchers for its maintenance and upgrading. In this paper, we developed an improved fusion method that can adapt the language model (LM) in ASR to the domain of air traffic control. Instead of using vocabulary in traditional fusion, this method uses the ATC instructions to improve the LM. The perplexity shows that the LM of the improved fusion is much better than that of the use of vocabulary. With vocabulary fusion, the CER in the ATC corpus decreases from 0.3493 to 0.2876. The improved fusion reduces the CER of the ATC corpora from 0.3493 to 0.2761. Although there is only a difference of less than 2% between the two fusions, the perplexity shows that the LM of the improved fusion is much better. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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18 pages, 3441 KiB

Open AccessArticle

An Experimental and Analytical Approach to Evaluate Transponder-Based Aircraft Noise Monitoring Technology

by Chuyang Yang and John H. Mott

Aerospace 2024, 11(3), 199; https://doi.org/10.3390/aerospace11030199 - 1 Mar 2024

Viewed by 1534

Abstract

Aviation is a vital modern transportation sector connecting millions of passengers globally. Sustainable aviation development holds substantial community benefits, necessitating effective management of its environmental impacts. This paper addresses the need for an accurate and cost-effective aircraft noise monitoring model tailored to non-towered [...] Read more.

Aviation is a vital modern transportation sector connecting millions of passengers globally. Sustainable aviation development holds substantial community benefits, necessitating effective management of its environmental impacts. This paper addresses the need for an accurate and cost-effective aircraft noise monitoring model tailored to non-towered general aviation airports with limited resources for official air traffic data collection. The existing literature highlights a heavy reliance on air traffic data from control facilities in prevailing aircraft noise modeling solutions, revealing a disparity between real-world constraints and optimal practices. Our study presents a validation of a three-stage framework centered on a low-cost transponder unit, employing an innovative experimental and analytical approach to assess the model’s accuracy. An economical Automatic Dependent Surveillance-broadcast (ADS-B) receiver is deployed at Purdue University Airport (ICAO Code: KLAF) to estimate aircraft noise levels using the developed approach. Simultaneously, a physical sound meter is positioned at KLAF to capture actual acoustic noise levels, facilitating a direct comparison with the modeled data. Results demonstrate that the developed noise model accurately identifies aircraft noise events with an average error of 4.50 dBA. This suggests the viability of our low-cost noise monitoring approach as an affordable solution for non-towered general aviation airports. In addition, this paper discusses the limitations and recommendations for future research. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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23 pages, 5532 KiB

Open AccessArticle

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

by Zheng Zhao, Jialing Yuan and Luhao Chen

Aerospace 2024, 11(2), 168; https://doi.org/10.3390/aerospace11020168 - 19 Feb 2024

Cited by 3 | Viewed by 2973

Abstract

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. [...] Read more.

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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14 pages, 3811 KiB

Open AccessArticle

Prediction of Hourly Airport Operational Throughput with a Multi-Branch Convolutional Neural Network

by Huang Feng and Yu Zhang

Aerospace 2024, 11(1), 78; https://doi.org/10.3390/aerospace11010078 - 15 Jan 2024

Cited by 2 | Viewed by 1625

Abstract

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how [...] Read more.

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations. Full article

(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))

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