Hierarchical Bayesian Method in the Study of Individual Level Behavior
In the Context of Discrete Choice Modeling with Revealed and Stated Preference Data
(Sprache: Englisch)
A major breakthrough in travel demand modeling in the early 1970's was modeling based on disaggregate (individual) level data (McFadden 2001).Although the disaggregate model focuses on individual level behavior, the estimated model parameters are fixed...
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A major breakthrough in travel demand modeling in the early 1970's was modeling based on disaggregate (individual) level data (McFadden 2001).
Although the disaggregate model focuses on individual level behavior, the estimated model parameters are fixed across individuals. To incorporate unobserved taste variations across individuals, recent developments allow for the parameters to vary across individuals, such as the Mixed Logit model, where the parameters are assumed to follow a distribution. The mixed logit model recognizes the differences among individuals, but it does not distinguish individuals who respond differently to travel service changes. This study focuses on the application of the Hierarchical Bayesian method to obtain individual level inferences. We demonstrate the advantage of this method by obtaining a more reasonable distribution of value of travel time
relative to the distribution obtained from the mixed logit model. In addition, the HB method helps us to combine information from both revealed and stated preference data, where the revealed preference data is limited to properties of only the chosen alternatives.
Klappentext zu „Hierarchical Bayesian Method in the Study of Individual Level Behavior “
A major breakthrough in travel demand modeling in the early 1970's was modeling based on disaggregate (individual) level data (McFadden 2001).Although the disaggregate model focuses on individual level behavior, the estimated model parameters are fixed across individuals. To incorporate unobserved taste variations across individuals, recent developments allow for the parameters to vary across individuals, such as the Mixed Logit model, where the parameters are assumed to follow a distribution. The mixed logit model recognizes the differences among individuals, but it does not distinguish individuals who respond differently to travel service changes. This study focuses on the application of the Hierarchical Bayesian method to obtain individual level inferences. We demonstrate the advantage of this method by obtaining a more reasonable distribution of value of travel time
relative to the distribution obtained from the mixed logit model. In addition, the HB method helps us to combine information from both revealed and stated preference data, where the revealed preference data is limited to properties of only the chosen alternatives.
Autoren-Porträt von Xiaojing Dong
Xiaojing Dongis an Assistant Professor of Marketing at the Leavey School of Business, Santa Clara University.She got her PhD from Northwestern University (Evanston, IL) and MS degree at MIT (Cambridge, MA).
Bibliographische Angaben
- Autor: Xiaojing Dong
- 2007, 80 Seiten, Maße: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: VDM Verlag Dr. Müller
- ISBN-10: 3836423537
- ISBN-13: 9783836423533
Sprache:
Englisch
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