A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling

The project team proposes a new multinomial probit-based model formulation for integrated choice and latent variable (ICLV) models, which has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this multinomial probit (MNP)-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. The project team's proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, the team's proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode choice will be designed to evaluate the ability of the MACML approach to recover model parameters.

• Record URL:
  http://ctr.utexas.edu/research/d-stop/research/projects/

Language

• English

Project

• Status: Completed
• Funding: $20000
• Contract Numbers:

  DTRT13-G-UTC58

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  Data-Supported Transportation Operations and Planning Center

  University of Texas at Austin
  Austin, TX  United States  78701
• Principal Investigators:

  Bhat, Chandra

• Start Date: 20130930
• Expected Completion Date: 20140930
• Actual Completion Date: 20140930
• Source Data: 102

Subject/Index Terms

• TRT Terms: Choice models; Estimation theory; Maximum likelihood method; Multinomial probits; Psychological aspects
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01579957
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: DTRT13-G-UTC58
• Files: UTC, RiP
• Created Date: Oct 28 2015 5:15PM