Generative AI Agents for Travel Behaviour: Applications in Surveys and Modelling
Bibliographic record
Abstract
<p dir="ltr">We explore the potential of Generative Artificial Intelligence (AI) agents created using open-access and locally hosted Large Language Models (LLMs) in replicating human survey behaviour and mode choice preferences in scenario-based travel surveys. The aim is to establish performance and validation benchmarks for utilizing AI agents in travel behaviour analysis, agent-based simulations, and other use cases. Accordingly, we developed a systematic scientific approach to assess the performance of seven open-access foundational LLMs, with parameters ranging from one to seventy billion, which can be generalized for creating and validating the performance of Generative AI agents in various applications.</p><p dir="ltr">The AI agents were developed using a zero-shot learning approach, incorporating both unrestricted sociodemographic and static prompting, as well as a dynamic restricted sociodemographic prompting strategy. The performance of these agents was validated against the human benchmark dataset, evaluating their effectiveness and reliability in capturing and replicating nuanced travel behaviour.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".