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1986•Transportation Research Record Journal of the Transportation Research BoardRequires access

CONSTRAINTS ON INDIVIDUAL TRAVEL BEHAVIOR IN A BRAZILIAN CITY

Joffre Swait́, Moshe Ben‐Akiva

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Abstract

In this paper the statistical and predictive performance of two disaggregate choice models that incorporate probabilistic choice set formation are compared with a standard logit specification. The empirical work is conducted with work mode choice data from a Brazilian city. For the type of travel demand analyzed it is found that, although statistically inferior to the probabilistic choice set specifications, the standard logit specification, allied with market segmentation, is a robust formulation in both statistical and predictive terms. Recommendations for future research work in probabilistic choice set modeling are presented.

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What this paper is about

In this paper the statistical and predictive performance of two disaggregate choice models that incorporate probabilistic choice set formation are compared with a standard logit specification. The empirical work is conducted with work mode choice data from a Brazilian city. For the type of travel demand analyzed it is found that, although statistically inferior to the probabilistic choice set specifications, the standard logit specification, allied with market segmentation, is a robust formulation in both statistical and predictive terms. Recommendations for future research work in probabilistic choice set modeling are presented.

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Available abstract

In this paper the statistical and predictive performance of two disaggregate choice models that incorporate probabilistic choice set formation are compared with a standard logit specification. The empirical work is conducted with work mode choice data from a Brazilian city. For the type of travel demand analyzed it is found that, although statistically inferior to the probabilistic choice set specifications, the standard logit specification, allied with market segmentation, is a robust formulation in both statistical and predictive terms. Recommendations for future research work in probabilistic choice set modeling are presented.

Key concepts: Probabilistic logic, Logit, Set (abstract data type), Mode choice, Econometrics, Logistic regression, Choice set, Statistical model

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