Abstract

Current implementation of new technologies for Communication, Navigation, Surveillance and Air Traffic Management (CNS/ATM systems) along with computational improvements on airborne and ground systems developed in the last two decades, point out the need for more strategic navigation and air-traffic control procedures based on four-dimensional (position plus time) trajectories. Moreover, the CNS/ATM infrastructure will help to achieve more shared real-time information among aircraft, airlines and air-traffic services providers (i.e. Air Traffic Control –ATCproviders, meteorological information providers and air space resources information providers). Then, general requirements for a next-generation of Air Traffic Management (ATM) system is that entities must share real-time information about aircraft state and intentions, air-space state and resources, meteorological conditions and forecast, etc. From this information coordinated and strategic actions should be carried out by them in order to achieve efficient and free of conflict 4D trajectories.

Abstract

traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.