Bayesian imputation of non‐chosen attribute values in revealed preference surveys |
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| Authors: | Simon Washington Srinath Ravulaparthy John M Rose David Hensher Ram Pendyala |
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| Institution: | 1. School of Urban Development, Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Queensland, Australia;2. Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, U.S.A.;3. Institute of Transport and Logistics Studies, University of Sydney, Sydney, New South Wales, Australia;4. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, U.S.A. |
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| 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. Copyright © 2012 John Wiley & Sons, Ltd. |
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| Keywords: | multinomial logit choice models imputation synthesized data Bayesian methods missing data analysis unobserved choice attributes |
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