This paper deals with the use of advanced statistics and data mining techniques to extract knowledge from large databases containing passenger and booking information (mainly the so called Passenger Name Records, PNR) of a major German airline company. Such knowledge is used to predict passenger behavior, which in turn is used to optimize capacity planning and improve overbooking management. The preliminary results, obtained on a sample of selected ﬂights, show that it is possible to successfully use PNR data and appropriate models to improve the overbooking optimization process. Critical success factors are: (a) data collection and preparation; (b) the method used for exploratory analysis and data reduction; (c) the forecasting methods: complex methods performed better, but simple methods might be preferred due to lower computational requirements and overall cost.
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