Optimizing neural networks for enhancing air traffic security

Abstract

This thesis contains the process and results related to optimizing a neural network to predict future positions of airplanes in the vicinity of airports. These predicted positions are then used to calculate future separation distances between pairs of airplanes. The predicted values of the separation distance are used to ensure adequate distances between adjacent aircrafts in the air and, if necessary, to create early warning alarms to alert air traffic control tower personnel about planes that may pass too near each other in the immediate future. The thesis covers three areas of work on this topic. The first section involves optimizing a neural network for Chicago O'Hare Airport. The second is related to gathering data on the performance of this network in different scenarios. These data can be used to determine if the different days/runways have different characteristics. The final phase of this document describes how to generalize the process used to build an optimized neural network for Chicago O'Hare airport in order to provide the capability to easily recreate the process for another airport.