How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-rich Survey and the 2017 National Household Travel Survey

A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation (Shaw, 2021; Soria and Mokhtarian, 2024) far outperforms other approaches such as using socio-demographic and land-use variables (Malokin et al., 2019) and targeted marketing variables (Shaw, 2021). The basic idea is to use one survey dataset (the “donor sample”) to train an ML function that predicts attitudinal factor scores using marker variables, and then apply that function to another dataset (the “recipient sample”) that contains the same marker variables, to impute attitude scores into it. This allows attitudinal information to be attached to the respondents in the recipient sample without measuring the whole set of attitudinal variables used to reveal the attitudinal factor structure in the donor sample.

• Record URL:
  • https://tbd.ctr.utexas.edu/research-product/how-effective-are-attitudinal-variables-at-improving-travel-behavior-models-evaluation-using-an-overlapping-sample-from-an-attitude-rich-survey-and-the-2017-national-household-travel-survey/

Language

• English

Project

• Status: Completed
• Funding: $200,000.00
• Contract Numbers:

  69A3552344815

  69A3552348320

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Understanding Future of Travel Behavior and Demand

  University of Texas
  Austin, TX  United States 
• Performing Organizations:

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Principal Investigators:

  Mokhtarian, Patricia

• Start Date: 20240601
• Expected Completion Date: 20250531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Attitudes; Datasets; Machine learning; Predictive models; Surveys
• Subject Areas: Data and Information Technology; Society; Transportation (General);

Filing Info

• Accession Number: 01989183
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 14 2026 3:45PM