Passenger Classification for an Airport Movement Forecast System

With new services provided by airlines and travel agencies passengers gained more flexibility, but also their visibility for airports and airlines was decreased. We describe the profiling component of a movement forecast system to increase passenger transparency. Therefore we present three approaches to learning classifiers and how they can be used for passenger classification. It is shown how the choice of attributes considered in classification influences classifier performance. This is used as a comparison criterion for learning algorithms.