Understanding demand for products and services is an integral part of many fields, including transportation, economics, marketing, public policy, and operations research. The methods used to model demand are as diverse as the fields in which they appear. Within the aviation industry, demand has typically been modeled using time-series, averaging methods, or simple probability distributions. As an example, consider the first generation revenue management systems designed in the 1990s. These systems initially modeled demand for each booking class on a flight using a Poisson or other statistical distribution. Booking classes were clearly defined and represented one or more products with similar prices and attributes. That is, each booking class corresponded to a distinct product offering that was defined by “fences” such as advance purchase, Saturday night stay, minimum stay, and other requirements. The more requirements associated with a product, the lower the price. Due to the distinct nature of the products, demand was assumed to be independent across booking classes and future demand for a booking class was predicted using historical demand for that booking class.


Discrete Choice Choice Probability Latent Class Model Discrete Choice Model Revenue Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author wishes to express her gratitude to Georgia Tech students Susan Hotle and Stacey Mumbower who helped compile many of the statistics reported in this chapter.


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Copyright information

© Springer Science+Business Media, LLC  2012

Authors and Affiliations

  1. 1.Georgia Institute of Technology AtlantaUSA

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