Research On Indicators Model Of Air Traffic Complexity

To the development of ATM system, Aritificial Intelligence and Automation are very important. Reducing air traffic system complexity, and reducing air traffic controllers real-time load, can improve the entire ATM system reliability and security. Through the study to the air traffic complexity indicators model, the airspace state can get relatively accurate forecast. This provides a scientific basis for the coming development and operation of ATM automation system.This paper presents some methods on how to use air traffic complexity indicators to identify sector status. Several complexity indicators, are selected and computed. Then, a principal component analysis (PCA) provides some results on the correlations between these indicators. Neural networks are used to find a relationship between complexity indicators and the actual sector configurations, and clustering analysis are used to find some new indicator to improve the initial model. In addition, to analyse some new model indicators, this paper presents a method of air sector clipping, including the data structure and algorithms.