No abstract

In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

The existing research on air traffic complexity ignores the effects of air traffic situation structure and, thus, cannot reflect the heterogeneous traffic density distribution in airspace. In this study, the structure of air traffic situation was characterized using the idea of community structure in complex networks. An aircraft cluster model was built, and an aircraft cluster discovery method based on depth-first traversal was proposed. The aircraft cluster division effect was comprehensively represented by cluster performance indices, including cohesion and stability. The routinely recorded radar data in two air traffic control sectors were collected to assess the cluster division results. Through statistics, the threshold intervals with 95% of best performance are 40–60 km and 20–50 km for the two sectors, respectively. The value 40 km was selected to further statistically characterize the aircraft clusters. Compared with K-means clustering, the proposed method does not require the predefined number of clusters and has high stability, which confirms its feasibility into cluster division in dynamic air traffic situation. The structural characteristics of aircraft clusters, including the average intra-cluster horizontal distance, number of clusters, and size and life cycle of clusters, were statistically analyzed. Comparison of cluster structures with the commonly used dynamic density index shows that in air traffic situation with relatively large number big size of clusters, the aircraft trajectory changes more frequently. Structural characterization of aircraft clusters is able to portray the nonuniformity of traffic density distribution, and contributes to comprehensive description of air traffic situation, thus providing a new prospect for analysis of air traffic complexity. Moreover, aircraft cluster division contributes to auto-identification of hot-spots on radar screen, and efficiently eliminates the workload imposed on controllers during judgment of these congestion hot-spots, thereby improving the air traffic operation efficiency.