26th INTERNATIONAL CONGRESS OF THE AERONAUTICAL SCIENCES PREDICTIVE MODELS OF THE NATIONAL AIRSPACE SYSTEM USING BAYESIAN NETWORKS

This paper focuses on a technique to model the National Airspace System which does not require selection of reference data and provides a frame-work to discover the complicated effects of con-gestion in one region of airspace on the operation of another. Since the interactions at this level are complex, only historical data are used in the anal-ysis. From these data, a Bayesian Network model of the system is learned that is capable of predict-ing the number of aircraft in certain regions of the airspace at a given time with greater accuracy than similar linear regression models. The model is also used to automatically determine which re-gions of the system are the most critical to over-all performance. The model is also used to auto-matically rank the regions of the system based on how important congestion in each region is to the overall performance of the system.