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

Washington, Simon, Ravulaparthy, Srinath, Rose, John M., Hensher, David and Pendyala, Ram (2014) Bayesian imputation of non-chosen attribute values in revealed preference surveys. Journal of Advanced Transportation, 48 1: 48-65. doi:10.1002/atr.201


Author Washington, Simon
Ravulaparthy, Srinath
Rose, John M.
Hensher, David
Pendyala, Ram
Title Bayesian imputation of non-chosen attribute values in revealed preference surveys
Journal name Journal of Advanced Transportation   Check publisher's open access policy
ISSN 0197-6729
2042-3195
Publication date 2014-01-01
Year available 2012
Sub-type Article (original research)
DOI 10.1002/atr.201
Open Access Status Not yet assessed
Volume 48
Issue 1
Start page 48
End page 65
Total pages 18
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Abstract 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.
Keyword Bayesian methods
Choice models
Imputation
Missing data analysis
Multinomial logit
Synthesized data
Unobserved choice attributes
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
 
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