How Effective are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-Sample Evaluation

Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models. Two main objections have been raised to their inclusion: they are too cumbersome to measure, and difficult-if-not-impossible to forecast. This project would continue a line of research that focuses on overcoming the first objection. Specifically, the plan is to use machine learning methods to train a prediction function on one survey dataset (the “donor sample”), and then apply that function to impute attitudes into another dataset (the “recipient sample”). This keeps the recipient survey less burdensome on the respondent, while allowing the dataset to receive attitudinal information that would otherwise be absent.

• Record URL:
  https://tbd.ctr.utexas.edu/research-product/how-effective-are-marker-variables-at-predicting-attitudinal-factor-scores-an-out-of-sample-evaluation/

Language

• English

Project

• Status: Active
• Funding: $187088
• 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 
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  Georgia Institute of Technology, Atlanta

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

  Mokhtarian, Patricia

• Start Date: 20231001
• Expected Completion Date: 20240531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Attitudes; Machine learning; Surveys; Travel behavior; Travel demand
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01917643
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 6 2024 4:14PM