基于复杂网络的空中交通复杂性识别方法

在空中交通管理中，识别空中交通复杂性是一项重要工作。目前的算法多采用飞机密度、机群、滞留程度等宏观指标对复杂性进行评价。利用复杂网络理论描述空中交通状况，将空域中的飞机视为节点，飞机与飞机之间距离小于彼此的机载防撞系统（ACAS）通信距离时开始构成连边，以此构建飞行状态复杂网络模型，可以更好地描述网络内部的微观特征。选取环边数、节点强度、平均聚类系数、介数中心性和网络效率等拓扑特性指标，对动态空中交通状况进行了研究。在此基础上，采用独立主元分析（ICA）在线识别空中交通复杂性，将交通顺畅的情况作为训练数据集进行处理，根据SPE统计量、I2统计量和I_e2统计量的变化来识别复杂性情况。仿真结果表明，所提方法可以较好地识别空中交通复杂性。 

Air traffic complexity recognition method based on complex networks

Identifying the complexity of air traffic is an important task in air traffic management. Most current algorithms are usually tested using some macro-indexes, such as aircraft density, aircraft clusters, stranded degree, and so on. In this paper, the air traffic situation is described from the perspective of complex networks: aircraft in airspace are regarded as nodes and edges form within Airborne Collision Avoidance System (ACAS) communication ranges. The dynamic air traffic situation is studied by selecting topological characteristic indexes such as loop numbers, node strength, average clustering coefficient, betweenness centrality and network efficiency. On this basis, Independent Component Analysis (ICA) is used to recognize air traffic complexity online and treat the smooth traffic as a training data set. The congestion is recognized according to the changes of SPE-statistic, I2-statistic and I_e2-statistic. The simulation results show that the proposed method has the ability to identify air traffic complexity well.