In this paper we present a neural network that is intended t o support airline marketing specialists in controlling seat allocations on flight departures. The focus of our investigation is the prediction of overbooking rates in order t o avoid that an aircrafl departs with empty seats when passengers who have booked seats do not participate in the flight. The neural network proposed t o solve the problem is an extension of the forward-only counterpropagation model. The network learns to approximate the mapping between the input data (the number of booked seats f o r each reservation class at distinct time periods prior to departure) and the desired output (the number of no-shows). The trained network is then used t o make the gredictions f o r the future. The feasibility of our approach is demonstrated by an efficient implementation. Ezperimental results obtained on real-life booking data f o r a particular flight indicate that the proposed neural network model is superior t o the standard forward-only counterpropagation model and quite competitive t o traditional, non-neural methods applied to the overbooking prediction problem.
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