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Passenger-based Predictive Modeling of Airline No-show Rates by Richard D. Lawrence; Se June Hong; Jacques Cherrier is a scholarly article available to read on EtoBox.

What is Passenger-based Predictive Modeling of Airline No-show Rates about?

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 noshows 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 passengerlevel 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

Author

Richard D. Lawrence; Se June Hong; Jacques Cherrier

Publisher

ACM

Published

2003

Language

EN