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In air traffic management, the controller uses the control instruction to adjust the aircraft status, and the pilot confirms by repeating the control instruction. The correct understanding of control instruction is of great significance to flight safety. This paper proposes a new method of air traffic control information extraction, which is based on pre-training and fine tuning of the pre-training language model. It uses transfer learning to extract regulatory information under the condition of small samples. This method can not only reduce the cost of training data annotation, but also improve the accuracy of information extraction. The simulation results show that the accuracy of the new model is not less than 98%, and the key information in the control instructions can be extracted effectively. This method can improve the intelligence of air traffic control system, assist controllers to understand the contents of control instructions, support flight conflict detection, and ensure air transport safety. [ABSTRACT FROM AUTHOR]

With the development of global air transport， air traffic services， including air traffic control， aeronautical operational control，etc， have entailed higher performance of aviation communication systems. As one of the new generation aviation communication system schemes， L‑band digital aeronautical communication system （L‑DACS） could achieve a better data transmission rate， spectrum utilization efficiency and capacity， solving scarcity of spectrum resources. This paper summarizes L‑DACS’s state‑of‑the‑art progresses and studies， specifically its technology evolution and standardization development. Then， a draft of layered system framework is introduced， covering network， datalink and physics levels. Finally， challenges that L‑DACS faces and its corresponding developing proposals are given. This review provides a reference for the research of aviation broadband communication. [ABSTRACT FROM AUTHOR]

This paper analyzes the feasibility of time-based metering operations for domestic flights in Japan, which have shorter flight times than international flights. For metering air traffic, aircraft are required to adjust their fly-over times at specified fixes. Speed control is typically used to adjust the fly-over time without stretching the lateral path. This study applies flight-level change in addition to speed control to increase the achievable airborne delay. The achievable airborne delay using speed control and flight-level change is evaluated statistically based on past actual radar tracking data. Through illustrative numerical simulations for the domestic flights to Tokyo International Airport, the achievable delays can be more than doubled and higher compliance rates are expected when changing the flight level in addition to speed control. Moreover, the potential benefit of saving fuel using speed control is evaluated, and a fuel savings of 2-4% is expected on average. Based on the analysis in this study, the maximum potential achievable delays and feasibility of time-based metering for domestic flights in Japan can be evaluated. [ABSTRACT FROM AUTHOR]

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline's operating network. The use of time-series data mining can be used to assess and quantify the impact of surface weather variables on EDCTs. A major hub airport in the United States, Charlotte Douglas International Airport, was chosen for the model development and assessment, and Vector Autoregression and Recurrent Neural Network models were developed. While both models were assessed to have demonstrated acceptable performance for the assessment, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather variables up to six hours before the prediction time period were used to develop the proposed lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant predictors for EDCT values by the Vector Autoregression and Recurrent Neural Network models. [ABSTRACT FROM AUTHOR]

The rapid growth of the airline industry has caused an enormous demand in the context of air transport and air traffic congestion in several hub airports. In order to alleviate this situation and resolve the imbalance between the arrival and departure rate, efficient runway usage in airport capacity management is an immediate and feasible solution as compared to airport expansion and runway construction. Air Traffic Control (ATC) operators could optimize their runway capacity by operating dynamic runway configuration in switch-mode runways based on the air and airport traffic conditions. A semi-mixed mode runway is considered in this paper, wherein some runways are configured for either landing or take-off operations, while others are operated in switch mode. The demand for arrival and departure is subject to the passenger's demand, flight availability, and timings, preferred flight schedule, and frequency of flight schedule service, and usually vary in different hours (peak and nonpeak hours). Given this feature, ATC can reconfigure the runway mode responding to the current demand for arrival and departure and further seize the runway capacity via a systematic approach. Under the semi-mixed mode situation, formulating the coordination of dynamic runway configuration planning and the Aircraft Sequencing and Scheduling Problem is proposed. The air traffic pattern in Hong Kong International Airport (HKIA) is used as a test case to evaluate the performance of this proposed model. Based on the test results, it was found that this dynamic runway configuration planning and semi-mixed runway design can utilize runway capacity more efficiently. In the numerical study, the dynamic runway configuration planning achieved 71.6%% and 37.08% reduction of flight tardiness than the two segregated runway systems (two landings and one take-off runways and one landing and two take-off runways) in HKIA. [ABSTRACT FROM AUTHOR]

In recent years, due to the close coupling between airports, airport risk propagation has become a huge challenge. However, it has not been fully understood on the network level. Airport risk can be transferred through other airports owing to connected resources. In this study, we consider two risk factors including airport delay and saturation and propose a risk coupling model based on a clustering algorithm to fit the index and form risk series. To understand the risk propagation mechanism, we build risk propagation networks based on the Granger Causality test, and we apply complex network theory to analyze the evolution of the risk propagation network. We study the regular pattern of risk propagation from perspectives of time and space. Through network analysis, we find four time stages in the risk propagation process and the participation of airports in risk propagation has a positive correlation with airport sizes. In addition, more large airports tend to prevent risk propagation in unoccupied and normal situations, while small airports perform better than large airports in busy situations. Via the conclusion, our work can assist airlines or air traffic managers in controlling the scale of risk propagation before its key time turning point. By identifying the critical airport level and related factors in risk propagation, they can also reduce single airport risk and risk participation through corresponding risk control measures, finally avoiding the large-scale spread of risk and reducing delay or cancellation of more flights. [ABSTRACT FROM AUTHOR]

The air transport market has a strategic character, promotes economic and social development, and has a strong correlation with the level of economic activity. The Air Traffic Management (ATM) system plays an essential role in air transportation and can be characterized as a sociotechnical system that is too complex to research through classical approaches such as systems engineering. In this sense, the application of Complex Network Theory (CNT) analysis and modeling paradigms is driven by the need to accommodate the growth of air traffic within an already saturated ATM infrastructure. The present work describes the development of a resilience evaluation model of ATM systems based on CNT, its metrics and analysis tools. The model was applied to the Brazilian ATM system, demonstrating its usefulness in identifying the air traffic control (ATC) agencies that have the greatest influence on the network. The results also showed that the Brazilian ATM network is resilient to random failures; however, it is particularly vulnerable to targeted attacks to the ATC agencies with the highest centrality. The research findings can be applied to prioritize the deployment of systems and equipment that enhance the resilience and operability of the Brazilian ATM system. [ABSTRACT FROM AUTHOR]

• A ST-Random Forest is proposed to predict flight delays. • The new method guaranties the influence of the air traffic network. • The spatial–temporal effect of the airport is considered. • The proposed ST-Random Forest model outperforms the benchmark models. In this paper, we propose a novel prediction framework (ST-Random Forest) for flight delay prediction from temporal and spatial perspective. We first apply complex network theory to extract the spatial feature of the aviation network at edge-, node-, and network-level. Furthermore, considering the temporal correlation of weather condition and airport crowdedness on flight delays, we create a prediction framework based on LSTM units to extract the temporal property of crowdedness and weather condition. Finally, we use the factors (e.g., spatial, temporal and extrinsic) that affect flight delays as inputs and apply Random Forest as classifier to predict flight delays. We apply and test our approach in a case study at China domestic flights between Jun and Aug 2016; after evaluation, we find that the accuracy of our proposed model reaches 92.39%. For the on-time samples, approximately 86% are correct identified; for the delayed samples, the classification accuracy reaches 95%. The ST-Random Forest model contributes to aviation authorities and airport regulators by creating real-time monitoring and high accuracy prediction system to alleviate flight delays and providing insightful suggestions to develop effective air traffic control strategies. [ABSTRACT FROM AUTHOR]

Analytical solutions for some problems of optimum with applications in air traffic and economics are given. For air traffic minimal distances between commercial airplanes (flight corridors) are imposed considering various trajectories: straightlines, orthodromes and loxodromes. Some other applications are related to target functions submitted to linear or nonlinear restrictions. Although specialized numerical codes exist the analytical solutions are useful giving a more clear understanding, suggesting new ways of approach and providing fast tests for preconception. [ABSTRACT FROM AUTHOR]

With the advent of the new security measures, today's airports have become increasingly complex and congested. Air and passenger traffic continues to increase; consequently, the need for intelligent design concepts is required. Unlike all other existing airport designs that focused exclusively on operational performance, this study focused on the development of a new airport terminal design methodology that takes a proactive approach to minimizing the effects of security disruptions while simultaneously maximizing operational performance and passenger flow. The study addressed the impact of security operations on both the design of airport facilities and passenger flows, and discussed options and scenarios to integrate these factors in obtaining improved performance. Simulation analysis results revealed that alternative designs can result in cost savings from evacuation time reductions of up to 10%. [ABSTRACT FROM AUTHOR]

The article presents a study concerning speed adjustment and optimal scheduling for arriving airplane in en route sector. It notes that the study has seen that one of the primary problems for busy airports during rush hours is flow control. It mentions that air traffic controllers must arrange the sequence of aircraft.