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 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 seg...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Richard D. Lawrence, Se June Hong, Jacques Cherrier
• Keywords: Conventional Historical Model, Data Mining, Direct Probability Model, KDD 2003, Passenger-level Model Outputs