Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?
This addresses scalability and cost issues in transportation planning and social science research, though it is incremental as it builds on existing synthetic data methods.
This study tackled the problem of high costs and scalability in traditional surveys by using Large Language Models (LLMs) to generate synthetic surveys for personal mobility preferences in Germany, demonstrating that LLMs can effectively capture complex dependencies between demographic attributes and preferences.
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.