Modern Data Visualization for Air Traffic Management

Abstract

Air traffic at airports is affected by various factors. The capacity of an airport and the demand at a certain point in time are serious parameters that account for a big extent to aircraft delay and related variables. It has been proven that weather is another important impact in this regard. Although weather cannot be controlled, the knowledge of how weather affects the air traffic at an airport can be very helpful to optimize air traffic management. Data mining promises to gain that knowledge. Usually, the very first step in data mining is data visualization. In this paper we discuss two new visualization techniques that allow to visualize aviation data and weather data in order to contribute to the optimization process. These modern multi-dimensional scaling techniques provide mappings of high-dimensional data to low-dimensional feature spaces. We will show some results on a practical application of a major European airport.