Air Traffic Research Articles

Exploring the Regional Structure of the Worldwide Air Traffic and Route Networks

The topological structure of the world air transportation network has been the subject of much research. However, to better understand the reality of air networks, one can consider the traffic, the number of passengers, or the distance between flights. This paper studies the weighted world air transportation network through the component structure, recently introduced in the network literature, by using the number of flights. The component structure is based on the community or multiple core-periphery structures and splits the network into local and global components. The local components capture the regional flights of these two mesoscopic structures (dense parts). The global components capture the inter-regional flights (links between the dense parts). We perform a comparative analysis of the world air transportation network and its components with their weighted counterparts. Moreover, we explore the strength and the s-core of these networks. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Centrality analysis reveals the difference between the top airports with high traffic and the top airports with high degrees. This difference is more pronounced in the global air network and the largest global component. Core analysis shows similitude between the s-core and the k-core for the local and global components, even though the latter includes more airports. For the world air network, the North and Central America-Caribbean airports dominate the s-core, whereas the European airports dominate the k-core.

Impact of Higher Airspace Operations on Air Traffic in Europe

Historically, higher airspace has been used for military exercises and as transit for space vehicles. Riding on commercial space operations’ coattails, more and more vehicles are under development that will make use of higher airspace resources. This will lead to increasing interactions with conventional air traffic since these new vehicles will have to transit through lower airspaces. The management of these operations is necessary to ensure the safe and practicable shared usage of these airspaces. This paper outlines an assessment of the impact of higher airspace operations on conventional air traffic in Europe. Initially, a synthesis of possible use cases was performed, and demand scenarios were developed that served as input to a fast-time simulation. The impact on air traffic was measured by means of flight efficiency parameters. The simulation results showed that the impact is dependent on the type of operation. High-altitude platform system flights and orbital launches cause the largest deviations in flight distance, flight duration and fuel consumption. Higher airspace operation parameters, including location, time, and duration, strongly affect the impact on the conventional air traffic.

A market-based framework for CO2 emissions reduction in China's civil aviation industry

This paper proposes a novel aviation CO2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO2 emissions and mitigate the effects of global warming.

Application of Aerometeorological Technology in Air Traffic Control—Taking Huatugou Airport as an Example

Application of Aerometeorological Technology in Air Traffic Control—Taking Huatugou Airport as an Example

A Study on Health Promotion Management Plan for Air Traffic Controllers and Pilots in Post-COVID-19: Focusing on Singing Bowl Meditation

A Study on Health Promotion Management Plan for Air Traffic Controllers and Pilots in Post-COVID-19: Focusing on Singing Bowl Meditation

The environmental impact assessment of greener trajectories: the GreAT project

GreAT (Greener Air Traffic Operations) is a project funded by the European Commission under the H2020 framework programme. The overall objective is to reduce the fuel consumption and gas emissions during “gate-to-gate” flight phases through developing and assessing environment-friendly air traffic operational concept, adaptive airspace and green trajectory optimization technologies, and supporting avionic systems. Based on the scientific description of the impact of aviation emissions on the climate, the work within GreAT is seeking the key factors of the impact about aviation emissions on climate change characteristics by using sensitivity analysis, such as greenhouse gases, pollutant gases and condensation, and then select these factors as environmental impact assessment indicators, including fuel consumption, aviation emissions, air quality and greenhouse effect, establishing a calculation model for evaluation indicators using the fuel consumption model, gas emission model and climate change model. System analysis methods are used to build an aviation emission environmental impact (EIA) assessment index system structure, apply environmental impact assessment indicators, construct a general environmental impact assessment index system, and propose a comprehensive assessment method for aviation environmental impact. The following step in the project is the environmental impact assessment of air traffic operations to determine how green air traffic performs. According to the existing air traffic operation patterns, the flight characteristics and trajectory characteristics of the aircraft are determined, and the environmental impact assessment index system is used to evaluate the environmental impact under the air traffic operation plan and the impact and improvement effect on climate change.

Comparative Characteristics of Psychophysiological Peculiarities of Operators of Unmanned Aircraft Systems and Air Traffic Control Personnel

Annotation. The article is devoted to the study and comparison of professionally important psychophysiological peculiarities of operators of unmanned aircraft systems (UAS) and air traffic control (ATC) personnel who are serving in the Armed Forces of Ukraine. The purpose of the study is to establish the kinship and differences in the psychophysiological characteristics of operators of UAS and ATC personnel in order to develop recommendations for conducting professional orientation and professional selection of such specialists. To achieve the goal, 142 operators of UAS and 71 specialists who perform duties in the process of ATC under the age of 40 during the period of inpatient flight medical examination on the basis of the Military Medical Clinical Center of the Central Region were selected. The examination was carried out with the help of blank methods and software and hardware complex for psychophysiological research «PFI-2». It is shown that operators of UAS are more prone to risk compared to ATC personnel. The difference in speed characteristics of operators of UAS and ATC personnel indicates higher requirements for decision-making by operators of UAS and greater exhaustion of to the indicators of the critical frequency of light flickers and the number of omission errors in the implementation of the evaluation of a complex visual-motor reaction, which proves that they are busier during the performance of official duties. The number of correct marks when performing the attention task is higher among ATC personnel, and the number of missed rings among operators of UAS is slightly higher compared to the order studied group. Such an effect may be related to the difference in the form of their professional activity during the performance of assigned tasks. The established difference in the level of psychological and psychophysiological parameters of operators OF UAS and ATC personnel can be used to develop criteria for monitoring professionally important qualities and professional selection of specialists in the specified professions.

THE EFFECT OF METEOROLOGICAL UNDERSTANDING OF AERODROME CONTROL TOWER PERSONNEL ON AIR TRAFFIC SERVICES AT MAKASSAR AIR TRAFFIC SERVICE CENTER

ATC personnel have an important role in the process of disseminating meteorological information for every phase of flight. The purpose of this study is to find out how much influence the meteorological understanding of ATC personnel has on air traffic services at Makassar Air Traffic Services Center. The quantitative research method is used by collecting data using questionnaires for ATC personnel and unstructured interviews with pilots at Sultan Hasanuddin International Airport in Makassar. The data processing used in this study is a correlation test, determination test, and simple linear regression. This study found the correlation coefficient is 0.79117 and the coefficient of determination is 62.6% which explains that understanding meteorology and air traffic services has a strong relationship and personnel ATC’s understanding of meteorology contributes 62.6% to air traffic services. Simple linear regression shows that for every additional 1 value of meteorological understanding, air traffic services will increase by 1.79111, the better the meteorological understanding of ATC personnel, the better the air traffic services at Makassar Air Traffic Services Center.

Flight Delay Propagation Prediction Based on Deep Learning

The current flight delay not only affects the normal operation of the current flight, but also spreads to the downstream flights through the flights schedule, resulting in a wide range of flight delays. The analysis and prediction of flight delay propagation in advance can help civil aviation departments control the flight delay rate and reduce the economic loss caused by flight delays. Due to the small number of data samples that can constitute flight chains, it is difficult to construct flight chain data. In recent years, the analysis of the flight delay propagation problem is generally based on traditional machine learning methods with a small sample size. After obtaining a large amount of raw data from the China Air Traffic Management Bureau, we have constructed 36,287 pieces of three-level flight chain data. Based on these data, we tried to use a deep learning method to analyze and forecast flight delays. In the field of deep learning, there are CNN models and RNN models that deal with classification problems well. Based on these two classes of models, we modify and innovate the study of the problem of flight delay propagation and prediction. Firstly, the CNN-based CondenseNet algorithm is used to predict the delay level of the three-level flight chain data. Based on this, the CondenseNet network is improved by inserting CBAM modules and named CBAM-CondenseNet. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 89.8%. Compared with the traditional machine learning method, the average prediction accuracy increased by 8.7 percentage points. On the basis of the CNN model, we also considered the superiority of the LSTM (Long Short-Term Memory network) considering the processing time sequence information, and then constructed the CNN-MLSTM network and injected the SimAM module to enhance the attention of flight chain data. In the experiment of flight delay propagation prediction, the accuracy rate is 91.36%, which is a significant improvement compared to using the CNN or LSTM alone.

A Wideband High-Gain Circularly-Polarized Dielectric Horn Antenna Equipped With Lamé-Axicon Stacked-Disk Lens for Remote Sensing, Air Traffic Control and Satellite Communications

A wideband high-gain circularly polarized (CP) shaped dielectric horn-lens antenna (SDHLA) operating in the frequency band between 6.7 and 18.2 GHz [fractional impedance bandwidth (FIBW) of 92.4%] with a 3-dB axial-ratio in the frequency range from 8.1 to 16.3 GHz [fractional axial-ratio bandwidth (FARBW) of 67.2%], is presented. The antenna, composed of a suitably shaped dielectric horn, integrated with a super-ellipsoidal-axicon dielectric lens made out of stacked thin dielectric disks, is mounted on a printed circuit board (PCB) where a broadband microstrip line terminated with a radial stub is used to excite a S-shaped slot through which the circular polarization is achieved. Parameterized 3D Lamé curves describe the optimal geometries of horn and lens profiles. The antenna features a peak realized gain exceeding 13.1 dBi that is beneficial in a variety of applications, such as digital video broadcasting (DVB), remote sensing, weather monitoring, satellite communications, and air traffic control. The full-wave electromagnetic solver CST Studio Suite™, based on a locally conformal finite integration technique (FIT), was employed to design and characterize the antenna. The antenna characteristics estimated by FIT were found to be in good agreement with the experimental measurements performed on an antenna prototype.

The Impact of Applying Deming's Model on Institutional Performance in Air Navigation: A Study on Quality and Customer Satisfaction

This study investigates the influence of applying Deming's model on the institutional performance of air navigation, with a specific focus on the concept of quality and its alignment with the present and future needs and expectations of customers. The research was conducted within the Department of Air Traffic in Kuwait, specifically within the Air Navigation Services Company, utilizing a sample of 206 employees. To achieve its objectives, the researcher developed a questionnaire as the primary research tool, designed to assess the relationship between the application of the Deming model and institutional performance. The study reveals several significant findings. Firstly, there exists a statistically significant relationship at a significance level of α≤0.05 between the practices of the Deming model and the promotion of administrative innovation within the Air Navigation Company in Kuwait. Additionally, the research demonstrates a statistically significant effect at the α≤0.05 level of Deming model practices on internal institutional innovation. Moreover, a similar statistically significant effect at α≤0.05 was observed for Deming Model Management practices on creating value within the Air Navigation Company in Kuwait. Among the key recommendations arising from this study is the importance of assessing the quality of air navigation services from the perspective of customers, particularly in light of the application of the Deming model. Understanding customer perceptions and expectations can play a pivotal role in further enhancing the quality and effectiveness of air navigation services. This research contributes valuable insights into the application of Deming's model in the context of air navigation, shedding light on its potential benefits in terms of innovation, quality, and value creation.

Advances in Resilience Engineering for Air Traffic Management Applications

As an emerging safety perspective, resilience engineering is attracting increasingly attentions in aerospace industry. Numerous researches have been carried out to explore ATM resilience properties from different aspects. Nevertheless, there appear to be significant variations on the research objects, issues concerned, methods employed and indexes used. As far as we know, there seems to be an absence of a dedicated literature review to explore the application status of resilience in ATM area. To this end, our work is concentrated on the research situation and progress of resilience in ATM. A wide range of databases are retrieved to obtain an extensive coverage of the literature. Subsequently, the research works are reviewed on the basis of different classifications followed by a brief introduction of related findings and implications. Attempting to provide some reference for further studies, promising directions are pointed out in view of the challenges and requirements. It is desired that this work may throw some light on further explorations of ATM resilience.

On Fatigue Detection for Air Traffic Controllers Based on Fuzzy Fusion of Multiple Features.

Fatigue detection for air traffic controllers is an important yet challenging problem in aviation safety research. Most of the existing methods for this problem are based on facial features. In this paper, we propose an ensemble learning model that combines both facial features and voice features and design a fatigue detection method through multifeature fusion, referred to as Facial and Voice Stacking (FV-Stacking). Specifically, for facial features, we first use OpenCV and Dlib libraries to extract mouth and eye areas and then employ a combination of M-Convolutional Neural Network (M-CNN) and E-Convolutional Neural Network (E-CNN) to determine the state of mouth and eye closure based on five features, i.e., blinking times, average blinking time, average blinking interval, Percentage of Eyelid Closure over the Pupil over Time (PERCLOS), and Frequency of Open Mouth (FOM). For voice features, we extract the Mel-Frequency Cepstral Coefficients (MFCC) features of speech. Such facial features and voice features are fused through a carefully designed stacking model for fatigue detection. Real-life experiments are conducted on 14 air traffic controllers in Southwest Air Traffic Management Bureau of Civil Aviation of China. The results show that the proposed FV-Stacking method achieves a detection accuracy of 97%, while the best accuracy achieved by a single model is 92% and the best accuracy achieved by the state-of-the-art detection methods is 88%.

RANCANGAN ALAT PENDETEKSI KETERSEDIAAN FLIGHT PROGRESS STRIP BERBASIS Internet Of Things (IoT) DI UNIT FDPS/RDPS JAKARTA AIR TRAFFIC SERVICE CENTER (JATSC)

In JATSC Radar Data Processing System and Flight Data Processing System (RDPS / FDPS) Unit has one of the systems, namely Enhance- Jakarta Automation Air Traffic Service (E-JAATS). This equipment plays an important role in aviation services. The Working Processing (CWP) controller has equipment, one of which is to produce a flight progress strip. Flight strip is equipment used by ATC to keep a record of instructions issued and then used as a representation of all flights in a particular sector. RDPS / FDPS unit technicians are tasked with ensuring the availability of the flight strip. One of the problems that occurred when doing On The Job Training at the Jakarta Air Traffic Service was that RDPS / FDPS technicians did not know the availability of the flight strip every time and waited for information from ATC when the flight strip ran out. Based on this situation, the authors want to design a flight strip availability detection tool using NodeMCU and an infrared sponsor that can be accessed via Blynk APP so that it can help technicians find out information on flight strip availability at any time and immediately make changes when the flight strip will run out.

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

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.

The Influence of the Resident Subsidy on Regional Carrier Economies and the Environment in the Canary Interisland Air Traffic Network

The existence of aviation subsidies can exacerbate congestion in airports that operate near their maximum threshold. One of the main operational issues in airports, that is, aircraft taxiing operations, could become more difficult and, therefore, produce negative effects on airport capacity management. Air carrier economies, touristic mobility (leisure travel), and the environment could also be negatively affected. Greenhouse gas emissions, passenger travel time, and air carriers’ fuel costs, for example, might rise due to aircrafts’ increased taxiing time. This paper seeks to analyse the impact of regional aviation subsidies for regional hub airports in the Canary Island air traffic network; specifically, the effect it has on airports’ capacity in terms of LTO (landing and taking off) cycle delays and its repercussions for passenger travel time, air carrier economics, and the environment. Therefore, to quantify the extent that the increased traffic from subsidies cause airport ground operations to delay flights, the disturbance between aircraft categories in landing and taking off operations must be considered. The disturbance between aircrafts will be evaluated by employing a simple model of landing intervals considering two aircraft categories (i.e., ATRs-72, ATRs-42, and B737/A320 aircraft families). The above-mentioned methodology provides an approximation of the average processing rate for take-off using the ultimate capacity concept for a variety of aircrafts landing on a single runway of the airport. As a result, it must be noted that there is a potential economic and environmental impact associated with the implementation of the subsidy increase for residents’ interisland travel and from the Canary Islands to the mainland. There is also a negative effect on runways for both airports due to the capacity constraints. These impacts, however, have to be balanced with the social and economic benefits that each regional inhabitant derives from the subsidy in terms of improvement of their mobility for any purpose.

Air Traffic Trajectory Operation Mode Mining Based on Clustering

When processing track sequences, it is time-consuming and difficult to separate clusters with substantial density variations to deal with the problem of classic clustering methods mining common flight patterns in airspace. To overcome these issues, this research proposes a clustering-based technique for mining air traffic trajectory operation patterns. The track data are first decoded and rebuilt using a motion-based track training approach; next, a compression based on a deep autoencoder (OFAE) is provided, allowing the model to deal with the high-dimensional trajectory vector containing derived information. The compressed trajectory data are made as compact and dense as feasible using the L21 norm constraint, which reduces the operation time and improves the performance of the clustering process. The compressed trajectory is then analyzed using a fast-clustering algorithm based on density peaks (DPCA). To save time, a more refined distance measurement technique is added into the model in order to achieve the usual aircraft operation mode in the terminal area. The accuracy of trajectory prediction can be improved by using the generated unitized and high-class similarity trajectory data.

Methodological and theoretical fundamentals of bankruptcy probability assessment of "UkSATSE" enterprise

In the economic sciences today there is a large number of developments in the field of analysis and forecasting of insolvent companies, which in turn helps to find and assess the criteria for the probability of bankruptcy of insolvent entities on the example of state enterprise “Ukrainian State Air Traffic Services Enterprise (UkSATSE)”. This paper is devoted to the reflection of the main methods and models for estimating the probability of bankruptcy of the enterprise, which were proposed by foreign and domestic scientists. In particular, it is noted that the assessment of the threat of bankruptcy on the model of Altman in our economic conditions can be considered as conditional, preliminary, especially if the business entity cannot be classified as medium or large. This model does not allow to obtain a fairly objective result. This is due to differences in the accounting of individual indicators, the impact on their formation of a number of external factors (primarily inflation), the mismatch between the book and market value of individual assets and other objective reasons. Such features determine the need to adjust the coefficients of significance of the indicators given in the Altman model, which in a developing economy is hardly possible. It is noted that in general, only financial ratios are used to predict the probability of bankruptcy, and the company's bankruptcy depends not only on the financial area. The calculated indicators do not include such coefficients as: return on assets, the degree of suitability of fixed assets, material consumption, instability of personnel - these are some of the main coefficients of the company. Methods of domestic scientist O. Tereshchenko will be suitable for our country. Thus, all models of the probability of bankruptcy have a serious drawback: they do not take into account the state of development of the industry and the country. The main directions and ideas in business are considered, the forecast of Eurocontrol concerning changes of traffic of flights for 4 years is resulted. The use of a discriminant model for estimating the probability of bankruptcy on the example of the enterprise state enterprise “UkSATSE” is analyzed. Based on all the data, an analysis and description of the shortcomings of the use of discriminant analysis is created. Recommendations for improving the state of the enterprise and timely forecast of the probability of bankruptcy are given.

READING ABILITY OF SENIOR AIR TRAFFIC CONTROL CADETS AS MEASURED BY ICAO ENGLISH LANGUAGE PROFICIENCY TEST AT CASEA OF MAKASSAR

This research is aimed to know the reading ability of Senior Air Traffic Control Cadets through ICAO English Language Proficiency Test. This research applied descriptive study. The population of the research was 87 cadets of D.III SATC CASEA Makassar which are divided into 4 classes, they are D.III SATC VI, D.III SATC VII, D.III SATC VIII A dan D.III SATC VIII B. The sample of the research took through their registration number consists of 24 cadets. They are cadets of D.III SATC VIII B. Procedure of collecting data that the researcher has applied was gave the reading test to cadets, and each cadet should answer the questions that given. The result of the research shows that the senior air traffic control cadets still have low ability in reading

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.