Coordinated by:
Research Questions
THALES
Consortium members:
York, Aachen, INNAXIS
How to identify combinations of events as they are happening and issue warnings to operators early enough to take action? In the continuous effort for ensuring increasing levels of safety, it is of utmost importance to understand the reasons behind the occurrence of operational errors.
Research Scope
During the operation of an ATM system, its components and systems produce a high volume of system events (e.g. health status of individual devices, temperature/proximity readings), and perform measurements (e.g. volume of traffic, structural complexity of the airspace). Of particular interest to safety management is the combinations of events (and their measurements) that can lead to scenarios where the safe operation of the system is compromised. The high volume of potentially interrelated events produced across the system, and the large number of possible combinations of events that can compromise safety render manual monitoring and management extremely challenging due to the high dimensionality of the overall system and the large amount of data produced. The need for automated mechanisms that can filter and organise high volumes of heterogeneous, incomplete or unreliable data in an intelligent manner is imperative.
COMPASS proposes data science techniques to tackle the problem of forecasting the occurrence of safety-related events, corresponding to situations in which separation minima is not respected, by analyzing data of real aircraft trajectories and planned intentions. The research focused on investigating the differences between two groups of events: events that may have evolved in safety-related events, but were avoided by the intervention of ATC, and those that actually ended up in a separation loss. While the concept of discriminating accidents from events that could have resulted in an accident has already been explored in the literature, COMPASS proposes the use of complex systems techniques to analyze the trajectories of aircraft, building a purely data-driven approach to the problem.
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Research Results
The project successfully developed a two-step procedure to overcome the major data science challenges, like heterogeneity and incompleteness of data, the size of data, the high dimensionality and the low frequency of safety events to be investigated. This novel two-step procedure includes the identification of a short list of relevant features, and construction of a global model by combination of those features. In particular, beyond the use of traditional features that describe the status of the airspace, the project used additional features that described the scenario (through its data) in much richer detailed: link density, maximum degree, number of connected components, dispersion of component sizes, efficiency, Eegenvector centrality, energy of the degree distribution and trajectory synchronization likelihood, among others. Therefore, focusing on the development of safety leading indicators, the project set up the basis on the use of Data Science techniques for potential uses like management of automation procedures and technology, development of new metrics tracking the evolution of airspace conditions or tactical safety management
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