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

Simon Washington, Srinath Ravulaparthy, John M. Rose, David Hensher, Ram Pendyala

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)48-65
Number of pages18
JournalJournal of Advanced Transportation
Issue number1
StatePublished - Jan 2014


  • Bayesian methods
  • choice models
  • imputation
  • missing data analysis
  • multinomial logit
  • synthesized data
  • unobserved choice attributes

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management


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