Discrete choice models

Michel Bierlaire
Intelligent Transportation Systems Program
Massachusetts Institute of Technology
E-mail: mbi@mit.edu
URL: http://web.mit.edu/mbi/www/michel.html

May 22, 1997

Abstract:

Discrete choice models have played an important role in transportation modeling for the last 25 years. They are namely used to provide a detailed representation of the complex aspects of transportation demand, based on strong theoretical justifications. Moreover, several packages and tools are available to help practionners using these models for real applications, making discrete choice models more and more popular.

Discrete choice models are powerful but complex. The art of finding the appropriate model for a particular application requires from the analyst both a close familiarity with the reality under interest and a strong understanding of the methodological and theoretical background of the model.

The main theoretical aspects of discrete choice models are reviewed in this paper. The main assumptions used to derive discrete choice models in general, and random utility models in particular, are covered in detail. The Multinomial Logit Model, the Nested Logit Model and the Generalized Extreme Value model are also discussed.

• Introduction
• Modeling assumptions
  • Decision-maker
  • Alternatives
  • Attributes
  • Decision rules
    • Neoclassical Economic Theory
    • The Luce model
    • Random Utility Models
• Random utility models
  • Assumptions on the random term
  • Assumptions on the deterministic term
• Multinomial logit model
• Nested logit model
  • Normalization of nested logit models
• Generalized extreme value model
• Conclusion
• Acknowledgments
• References
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