Testing the dogit model with aggregate time-series and cross-sectional travel data

Gaudry, Marc; Wills, Michael J.

doi:10.1016/0191-2615(79)90034-1

Cited by 22 publications

(6 citation statements)

References 8 publications

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“…Kitamura and Lam (1984) and Swait and Ben-Akiva (1986) show that the incorrect representation of constraints in such captivity situations may lead to serious errors in the prediction of choice behavior. The dogit model has been used with some success in travel-mode choice analysis (Gaudry and Wills 1979;Swait and Ben-Akiva 1987b), but appears not to be well suited for destination choice modeling, where the operation of any constraints is much more complex than simple captivity to some alternative (Swait and Ben-Akiva 1987a;Thill 1992).…”

Section: Destination Choice Modelsmentioning

confidence: 99%

Travel‐Time Constraints on Destination‐Choice Sets

Thill

Horowitz

1997

Geographical Analysis

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Section: Destination Choice Modelsmentioning

confidence: 99%

Travel‐Time Constraints on Destination‐Choice Sets

Thill

Horowitz

1997

Geographical Analysis

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“…The extra costs and efforts are judged by many researchers as unwarranted. The dogit model (Gaudry and Dagenais 1979), developed specifically to overcome the IIA property of the logit model, brought only marginal improvement over the latter in an intercity model choice application (Gaudry and Wills 1979). Thus, despite its IIA property, the logit formulation is a simple, economical, and practical technique.…”

Section: Logit Versus Probit and Dogit Analysesmentioning

confidence: 99%

Review of advantages and applications of disaggregate mode choice models in intercity passenger travel

Ghoneim¹,

Sargious²

1987

Can. J. Civ. Eng.

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The use of disaggregate models in modelling intercity passengers mode choice behaviour has emerged over the past 20 years. In an attempt to encourage this use, the present paper addresses the advantages and disadvantages of the disaggregate approach as opposed to the conventional aggregate techniques. The results of a literature review in this regard indicate that disaggregation is statistically and behaviourally necessary to model human travel behaviour while being sensitive in selecting the unit of analysis. The paper also compares the logit analysis with other modelling techniques available for application in order to identify the most suitable one. A critical review of previous modelling efforts in the U.S. and Canada, based on the disaggregate logit analysis is presented to demonstrate the applicability of this technique to modelling intercity passengers mode choice behaviour. Some modelling drawbacks and the general findings of the studies are emphasized to provide useful insight for future modelling considerations.

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“…A model that can account for this is the Dogit model (Gaudry & Dagenais, 1979). In spite of its apparent applicability in numerous situations, the Dogit model has seldom been applied in practice (for examples, see Gaudry & Wills, 1979;Gaudry, 1980;Tse, 1987;Bordley, 1990;Chandrasekharan, McCarthy & Wright, 1994;Kannan & Yim, 2001). The Dogit model contains additional parameters to the standard MNL model, which are often interpreted as 'captivity', 'loyalty', 'gravity' or 'preference' coefficients.…”

Section: Introductionmentioning

confidence: 99%

The Dogit Ordered Generalized Extreme Value Model

Fry

Harris

2005

Aus NZ J of Statistics

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This paper introduces the Dogit ordered generalized extreme value (DOGEV) model, for handling discrete variables that are ordered and heterogeneous. In particular, the DOGEV model can be applied to questionnaire responses on questions allowing a discrete set of ordered possible responses, where there is a preference for particular responses and possibly multiple modes in the data. The DOGEV model combines a model for choice set generation with the ordered generalized extreme value model. The paper illustrates the model using two empirical examples: a model of inflationary expectations and a model for students' evaluations of teaching.

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Testing the dogit model with aggregate time-series and cross-sectional travel data

Cited by 22 publications

References 8 publications

Travel‐Time Constraints on Destination‐Choice Sets

Travel‐Time Constraints on Destination‐Choice Sets

Review of advantages and applications of disaggregate mode choice models in intercity passenger travel

The Dogit Ordered Generalized Extreme Value Model

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