# Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?

arXiv: https://arxiv.org/abs/2501.13955 · HTML: https://mlsanity.com/paper/2501.13955/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 5 of 5)

- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [A Survey on Data Synthesis and Augmentation for Large Language Models](https://mlsanity.com/paper/2410.12896/) (2024)
- [Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study](https://mlsanity.com/paper/2412.13169/) (2024)
- [An evaluation framework for synthetic data generation models](https://mlsanity.com/paper/2404.08866/) (2024)
- [Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs](https://mlsanity.com/paper/2412.13233/) (2024)

## Built on by (most cited first, 3 of 3)

- [Aligning LLM agents with human learning and adjustment behavior: a dual agent approach](https://mlsanity.com/paper/2511.00993/) (2025)
- [The conditional superiority of fast silicon sampling](https://mlsanity.com/paper/2608.14079/) (2026)
- [Understanding Reliability in LLM-based Human Behavior Simulation](https://mlsanity.com/paper/2609.25066/) (2026)

## Abstract (arXiv)

This study explores the potential of Large Language Models (LLMs) to generate artificial surveys, with a focus on personal mobility preferences in Germany. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods, such as high costs, inefficiency and scalability challenges. A novel approach incorporating "Personas" - combinations of demographic and behavioural attributes - is introduced and compared to five other synthetic survey methods, which vary in their use of real-world data and methodological complexity. The MiD 2017 dataset, a comprehensive mobility survey in Germany, serves as a benchmark to assess the alignment of synthetic data with real-world patterns. The results demonstrate that LLMs can effectively capture complex dependencies between demographic attributes and preferences while offering flexibility to explore hypothetical scenarios. This approach presents valuable opportunities for transportation planning and social science research, enabling scalable, cost-efficient and privacy-preserving data generation.