Aerospace

Research

17 pages, 680 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 (registering DOI) - 8 May 2024

Viewed by 103

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))

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 261

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

Viewed by 578

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 788

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 823

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 775

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 1028

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 1 | Viewed by 1673

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 1 | Viewed by 972

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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