document: Passenger-based predictive modeling of airline no-show rates Airlines routinely overbook flights based on the expectation that some fraction of booked passengers will not show for each flight . Accurate forecasts of the expected number of no-shows for each flight can increase airline revenue by reducing the number of spoiled seats ( empty seats that might otherwise have been sold ) and the number of involuntary denied boardings at the departure gate . Conventional no-show forecasting methods typically average the no-show rates of historically similar flights , without the use of passenger-specific information . We develop two classes of models to predict cabin-level no-show rates using specific information on the individual passengers booked on each flight . The first of these models computes the no-show probability for each passenger , using both the cabin-level historical forecast and the extracted passenger features as explanatory variables . This passenger-level model is implemented using three different predictive methods : a C4 .5 decision-tree , a segmented Naive Bayes algorithm , and a new aggregation method for an ensemble of probabilistic models . The second cabin-level model is formulated using the desired cabin-level no-show rate as the response variable . Inputs to this model include the predicted cabin-level no-show rates derived from the various passenger-level models , as well as simple statistics of the features of the cabin passenger population . The cabin-level model is implemented using either linear regression , or as a direct probability model with explicit incorporation of the cabin-level no-show rates derived from the passenger-level model outputs . The new passenger-based models are compared to a conventional historical model , using train and evaluation data sets taken from over 1 million passenger name records . Standard metrics such as lift curves and mean-square cabin-level errors establish the improved accuracy of the passenger-based models over the historical model . All models are also evaluated using a simple revenue model , and it is shown that the cabin-level passenger-based model can produce between 0.4 % and 3.2 % revenue gain over the conventional model , depending on the revenue-model parameters .
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