Aircraft trajectory generation is a widely addressed problem with applications including emergency trajectory generation, collision risk models, air traffic flow and capacity management or airspace design. State of the art methods to generate individual trajectories and optimise some performance or emergency criterion may lack of realism with respect to common situations implemented by air traffic controllers. On the other hand, statistical data-driven methods to generate aircraft trajectories excel at imitating operational practice but may be difficult to implement even in simulations due to aircraft performance limitations. This contribution proposes a common baseline to compare literature and bleeding-edge methods to generate air traffic trajectories. Keeping in mind that the most appropriate criterion should always depend on the targeted application, we present here an extensive set of metrics to evaluate the quality of generated trajectories, before assessing two generation methods in light of these indicators. @en

Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather.

@en