Identification and Characterization of Traffic Flow Patterns for UTM application

The current airspace has limited resource, and the widespread use of Unmanned Aircraft System (UAS) is increasing the density of civilian aircraft that is already crowded with manned aerial vehicles. This increased density in airspace demands to improve the safety, efficiency and capacity of airspace while considering all uncertain parameters that may cause hinderance in aircraft movement like weather and dynamic fluctuations. A systematic analysis of correlations between events and their impacts in air traffic network is a considerable challenge. This paper proposes a methodology that characterizes and identifies the patterns of Unmanned Traffic Management (UTM) airspace based on the analysis of simulated data to improve the performance of UTM network as well as ensuring its safety and capacity. Some sets of metrics are defined to identify the airspace characteristics that include airspace density, capacity and efficiency. The data analysis carried out here, will support risk analysis and improve trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather conditions, loss of safe distances, UAVs’ performance, emergency services and airspace structures that may cause deviations from their standard paths.