Measuring the Complexity of Air Traffic Based on Complex Networks

The increasing complexity of air traffic and the rising demands on air traffic control have underscored the importance of thoroughly characterizing aerial traffic patterns and optimizing control allocation processes within air traffic management. Current studies commonly assess complexity using macroscopic indicators, including the number of aircraft and traffic density. Recently, researchers have turned to employing complex network theory to depict air traffic conditions, viewing aircraft in the airspace as nodes within a network. Edge is established between aircraft when their distance is less than the range of their airborne collision avoidance system (ACAS). Therefore, we construct a flight status network model. By analyzing topological characteristics like node average strength, edge density, average weighted clustering coefficient, average path length, and network efficiency, we have explored the intricacies of air traffic conditions. Simulation results indicate that these metrics offer a comprehensive evaluation of factors such as node proximity, node centrality, local connectivity, and overall network cohesion, providing a more detailed and holistic depiction of air traffic complexity compared to conventional traffic flow statistics.