Transportation (2003)

Volume: 30, Issue: 2, Pages: 133-176

Abstract

The mixed logit model is considered to be the most promising state of the art

discrete choice model currently available. Increasingly researchers and

practitioners are estimating mixed logit models of various degrees of

sophistication with mixtures of revealed preference and stated choice data. It is

timely to review progress in model estimation since the learning curve is steep

and the unwary are likely to fall into a chasm if not careful. These chasms are very

deep indeed given the complexity of the mixed logit model. Although the theory is

relatively clear, estimation and data issues are far from clear. Indeed there is a

great deal of potential mis-inference consequent on trying to extract increased

behavioural realism from data that are often not able to comply with the demands

of mixed logit models. Possibly for the first time we now have an estimation

method that requires extremely high quality data if the analyst wishes to take

advantage of the extended behavioural capabilities of such models. This paper

focuses on the new opportunities offered by mixed logit models and some issues

to be aware of to avoid misuse of such advanced discrete choice methods by the

practitioner.

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Cite this document (BETA)

Hensher, D. A., & Greene, W. H. (2003). The Mixed Logit Model: The State of

Practice. Transportation, 30(2), 133-176. Retrieved from

http://www.springerlink.com/link.asp?id=103007

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