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Integrating psychometric indicators in latent class choice models
Highlights A method to introduce psychometric indicators into integrated latent class and mode choice models is proposed. Psychometric indicators are related to the attributes of the decision maker through an ordinal logit measurement equation. The method is tested in two mode-choice case studies, identifying two latent classes in each of them. Use of psychometric indicators produces different estimates and better behavioural interpretation of classes.
Abstract Latent class models are a convenient and intuitive way to introduce taste heterogeneity in discrete choice models by relating attributes of the decision makers with unobserved behavioral classes, hence allowing for a more accurate market segmentation. Estimation and specification of latent class models can be improved with the use of psychometric indicators that measure the effect of unobserved attributes in the individual preferences. This paper proposes a method to introduce these additional indicators in the specification of integrated latent class and discrete choice models, through the definition of measurement equations that relate the indicators to attributes of the decision maker. The method is implemented for two mode-choice case studies and compared with alternative methods to introduce indicators. Results show that the proposed method generates significantly different estimates for the class and choice models and provide additional insight into the behavior of each class.
Integrating psychometric indicators in latent class choice models
Highlights A method to introduce psychometric indicators into integrated latent class and mode choice models is proposed. Psychometric indicators are related to the attributes of the decision maker through an ordinal logit measurement equation. The method is tested in two mode-choice case studies, identifying two latent classes in each of them. Use of psychometric indicators produces different estimates and better behavioural interpretation of classes.
Abstract Latent class models are a convenient and intuitive way to introduce taste heterogeneity in discrete choice models by relating attributes of the decision makers with unobserved behavioral classes, hence allowing for a more accurate market segmentation. Estimation and specification of latent class models can be improved with the use of psychometric indicators that measure the effect of unobserved attributes in the individual preferences. This paper proposes a method to introduce these additional indicators in the specification of integrated latent class and discrete choice models, through the definition of measurement equations that relate the indicators to attributes of the decision maker. The method is implemented for two mode-choice case studies and compared with alternative methods to introduce indicators. Results show that the proposed method generates significantly different estimates for the class and choice models and provide additional insight into the behavior of each class.
Integrating psychometric indicators in latent class choice models
Hurtubia, Ricardo (author) / Nguyen, My Hang (author) / Glerum, Aurélie (author) / Bierlaire, Michel (author)
Transportation Research Part A: Policy and Practice ; 64 ; 135-146
2014-03-11
12 pages
Article (Journal)
Electronic Resource
English
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