Predicting the demand for future flights accurately is highly important in order to ensure the survival of any airline. In order to reliably forecast the number of potential passengers Revenue Management forecasting methods need error-free true demand data. The true demand for a booking class is the number of passengers willing to purchase the ticket assuming the booking class is always available for purchase. However, due to aircraft's physical capacity limitations and historical Revenue Management decisions, the collected booking data seldom represents true demand. Adding to the true demand modelling complexity, airline customers' preferences and experiences vary significantly. Not account for these phenomena the true demand may result in erroneous predictions of the true demand. In addition, with similar options offered by various airlines the substitution patterns manifest strongly in observed booking numbers. To account for the varying preferences of passengers and for correlations between products we introduce a novel Segment Specific Cross Nested Logit Model with Brand Loyalty. The model explicitly handles substitution patterns between all the market options, allows tracking changing customers' preferences and accounts for their past experience. Using data from a Stated Preference experiment conducted among 360 Australians we showed that our discrete choice model constantly outperformed the more simplistic methods by 0.04 to 0.061 in terms of the adjusted R2, attaining results on par with more sophisticated models. At the same time our model was only marginally more computationally expensive than the simpler models. In this thesis we also propose a novel unconstraining model. The Market Spill Recapture Model delivers true demand estimates for all the booking classes offered by all the airlines in a specific market. Using an agent based booking simulator developed for this research, with discrete choice models from the Stated Preference experiment driving virtual passengers decisions, we simulated booking data for six virtual airlines. The Segment Specific Cross Nested Logit Model with Brand Loyalty was used to model demand interactions in our unconstraining model. For the simulated data our model delivered true demand estimates that were between 1 percent to 10 percent more accurate than the Multi-Flight Recapture Method.
Dr Cheng-Lung (Richard) Wu
Dr Tomasz Drabas