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

TL;DR

This paper investigates using Large Language Models to generate synthetic surveys for personal mobility preferences, aiming to improve scalability and cost-efficiency over traditional methods.

Contribution

It introduces a novel Persona-based approach for synthetic survey generation using LLMs and compares it with existing methods, demonstrating its effectiveness.

Findings

LLMs can accurately model demographic-preference dependencies

Persona-based synthetic surveys outperform other methods in realism

Approach enables scalable and privacy-preserving data generation

Abstract

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.