How Effective are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-sample Evaluation
Despite the fact that 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 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.
Language
- English
Project
- Status: Completed
- Funding: $187088
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Contract Numbers:
69A3552344815 and 69A3552348320
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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
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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: 20240831
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Attitudes; Data files; Machine learning; Predictive models; Surveys; Travel behavior; Travel demand
- Subject Areas: Planning and Forecasting; Society; Transportation (General);
Filing Info
- Accession Number: 01933301
- Record Type: Research project
- Source Agency: Center for Understanding Future of Travel Behavior and Demand
- Contract Numbers: 69A3552344815 and 69A3552348320
- Files: UTC, RIP
- Created Date: Oct 10 2024 3:50PM