Bayesian imputation of non-chosen attribute values in revealed preference surveys

Washington, Simon, Ravulaparthy, Srinath, Rose, John M., Hensher, David, & Pendyala, Ram (2014) Bayesian imputation of non-chosen attribute values in revealed preference surveys. Journal of Advanced Transportation, 48(1), pp. 48-65.

View at publisher


Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.

Impact and interest:

0 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 69748
Item Type: Journal Article
Refereed: Yes
Keywords: Multinomial logit, Choice models, Imputation, Synthesized data, Bayesian methods, Missing data analysis, Unobserved choice attributes
DOI: 10.1002/atr.201
ISSN: 01976729
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 02 Apr 2014 23:56
Last Modified: 02 Apr 2014 23:56

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page