Weather obstacles in the airspace can interfere with an aircraft’s flight plan. Pilots, assisted by air traffic controllers (ATCs), perform avoidance maneuvers that can be optimized. This paper addresses the generation of alternative aircraft trajectories to resolve unexpected events. The authors propose a solution based on the RRG algorithm, K-means clustering, and Dynamic Time Warping (DTW) similarity metric to address the problem. The mixed algorithm succeeds in generating a set of paths with diversity in an obstacle constrained airspace between Paris-Toulouse and London-Toulouse airports. This tool could help to reduce the workload of pilots and ATCs when such a situation arises.

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Many transportation networks have complex infrastructures (road, rail, airspace, etc.). The quality of service in air transportation depends on weather conditions. Technical failures of the aircraft, bad weather conditions, strike of the company’s staff cause delays and disrupt traffic. How can the robustness of such networks be improved? Improving the robustness of air transportation would reduce the cascading delays between airports and improve the passenger journey. Many studies have been done to find critical links and nodes, but not so many analyze the paths. In this paper, we propose a new method to measure network robustness based on alternative paths. Besides improving the robustness of the French (respectively Turkish Airlines and European) low-cost flight network by 19% (respectively 16% and 6.6%), the method attempts to show the relevance of analyzing the network vulnerability from a path-based approach.

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A multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong–Hong Kong–Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS.

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Inefficiencies in flight operations, like deviations and non-optimal flight speed or altitude, are directly linked to flight emission inefficiencies. Quantifying these emission inefficiencies and studying potential mitigation strategies is certainly beneficial for the sustainability of the aviation industry. In this paper, we analyze emission inefficiencies in Dutch and French airspaces using flight data from 2019. The emission inefficiency analysis quantifies the excess carbon emissions for each flight by comparing its emissions with a set of optimal alternative trajectories. We find that around 19% of excess emissions existed in 2019 within the airspace of interest. We also study the potential reduction of emissions by replacing short-range flights with electric aircraft. We propose a simple electric aircraft energy model and relate that to emissions in electric generations in different countries. We find that besides the significant increase in air traffic demand caused by the limited capacities of electric flights, the emissions caused by electricity generation cannot be neglected. Significant reductions can only be achieved when emissions caused by electricity generation are low, as is the case currently in France. However, more emissions can be indirectly generated if the electricity used to power the future electric aircraft is itself produced from high emission sources, as is the case currently in the Netherlands. The paper also provides further insights and recommendations on the data sources, research approach, and future research for aviation sustainability. @en