Does AI based synthetic travel behavior data replicate human cognitive biases?

This project will develop methods to test for cognitive biases in artificial intelligence (AI)-generated synthetic data used as substitutes for human survey responses in travel behavior analyses. Moving beyond recent benchmarking and prompt engineering work, this project will ask: even when synthetic data look statistically reasonable, do they reproduce canonical cognitive biases that influence travel choices? In other words, the research team tests whether synthetic data are behaviorally realistic, not merely statistically plausible. The team will implement a compact, transportation-specific battery of experimentally controlled stated-choice and judgment tasks to test for biases such as status quo bias, reference dependence and loss aversion around time/cost changes, framing, anchoring, and zero-price effects in toll/parking/fare presentation, and ambiguity aversion in travel-time reliability. The team will field this battery in parallel to (i) a commercial synthetic panel product (e.g., Qualtrics) and (ii) large language model (LLM)-based agents under multiple prompting/persona strategies, comparing each to a modest human reference data set. The human data may comprise existing data sets or data newly collected from known human respondents, as long as question wording is consistent with the AI data collection. The direction and magnitude of any cognitive biases demonstrated in the synthetic data will be compared with those in the human reference data.

Language

• English

Project

• Status: Active
• Funding: $150,000.00
• 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:

  University of Texas at Austin

  Austin, TX  United States  78712
• Performing Organizations:

  University of Washington, Seattle

  Civil and Environmental Engineering Department
  201 More Hall, Box 352700
  Seattle, WA  United States  98195-2700
• Principal Investigators:

  MacKenzie, Don

• Start Date: 20260916
• Expected Completion Date: 20270915
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bias; Data quality; Travel behavior
• Subject Areas: Data and Information Technology; Transportation (General);

Filing Info

• Accession Number: 02002875
• Record Type: Research project
• Source Agency: Center for Understanding Future of Travel Behavior and Demand
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: Sep 18 2026 10:45AM