Close parallel runway operations play a critical role in maximizing airport capacity and facilitating efficient aircraft arrivals and departures. However, the close proximity of parallel runways presents unique challenges during landing operations, including the risk of runway incursions, wake turbulence, and communication issues. This research paper aims to conduct a comprehensive survey of existing studies on close parallel runway landing operations, analyzing the various approaches employed by airports worldwide. The objective is to identify the most effective method for managing very closely recommendations. The survey encompasses factors such as runway spacing, air traffic control procedures, knowledge, and the identification of best practices. This study aims to contribute to the enhancement of safety and operational efficiency in close parallel runway environments. The findings of this survey will be valuable for airport operators, air traffic control agencies, and aviation authorities in making informed decisions.
As an essential component to realize the concept of Trajectory-based Operation, accurate trajectory prediction plays a crucial role in enabling the Air Traffic Management system to anticipate potential hazards and ensure safe operations. However, the trajectory prediction task faces significant challenges due to the multi-dimensional characteristics of trajectory data and the susceptibility of flight performance to external factors, which result in high uncertainty. To address these challenges, this paper proposes a Transformer-based trajectory prediction model that leverages the attention mechanism to identify key factors, enhancing its ability to extract diverse information from the data. The model is thoroughly evaluated using real trajectory datasets, and the simulation results demonstrate its effectiveness in predicting four-dimensional trajectories. Notably, compared to improved attention-based models and single recurrent neural network algorithms, the proposed model demonstrates higher prediction accuracy and superior performance in parallel sequential data processing. This lays a robust foundation for subsequent conflict detection and decision-making processes.
With the increasing complexity of air traffic, the operational characteristics of flights remain largely unexplored. In particular, the revision of Scheduled Flight Block Time (SFBT) heavily relies on statistical analysis of historical data. Therefore, the objective of this paper is to propose a method for analyzing flight operation characteristics from a spatial-temporal perspective. To achieve this, the DBSCAN algorithm was employed to uncover spatial aggregation patterns among flight segments. Additionally, the K-Means algorithm was utilized to investigate the periodicity of flight block time. Based on our findings, it is observed that the majority of airport segments can be categorized into 4-5 distinct groups. Furthermore, it was discovered that taxi time exhibits a higher degree of periodicity compared to flight air time. Overall, these results provide valuable insights into the characteristics of flight operations, shedding light on the overlooked aspects of air traffic management.
The optimization research on air route networks is significant in achieving low-carbon and environmentally friendly operations. However, existing studies on air route networks often lack consideration for this objective. To address this gap, this study conducts an in-depth analysis of the existing problems in the air route networks in the central and southern regions. Based on this analysis, a green air route network optimization model is constructed, comprehensively considering the operational costs, flight conflicts, and emissions of greenhouse gases and pollutants in the aviation sector. Genetic algorithms are employed to solve the optimization model. The empirical analysis is then conducted using a selected portion of the air route networks in the central and southern regions. The experimental results demonstrate that the optimized air route network can significantly reduce operational costs and environmental impacts. Specifically, the optimized air route network achieves a 6.18% reduction in operational costs and a 6.20% decrease in greenhouse gas and pollutant emissions. Although the change in the flight conflict coefficient is minimal, there is overall improvement, thus validating the effectiveness of the proposed model.
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