Synthetic data sets with non-constant substitution patterns for fare class choice

The article provides a theoretical framework for the generation of synthetic discrete choice datasets for fare class choice in airline revenue management. The necessity of this research arises from simplifying assumptions regarding demand modeling that are commonly made in airline revenue management optimization models. By applying demand models with more flexible substitution patterns, like the Nested Logit (NL) model, it is possible to account for dependencies between offered fare products and their influence on airline revenue. (A)

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 32-55
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01531763
  • Record Type: Publication
  • Source Agency: Bundesanstalt für Straßenwesen (BASt)
  • Files: ITRD
  • Created Date: Jun 3 2014 3:38AM