Behaviorally Realistic Urban Mobility Synthesis via Fine-Tuned Large Language Models

In urban science, understanding mobility patterns is essential for improving the quality of life and designing livable, efficient, and sustainable cities. However, collecting such data through user tracking or travel surveys poses challenges due to privacy concerns, non-compliance, and high cost. This work proposes an AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), leveraging their background knowledge and text generation capabilities. We evaluate the effectiveness of this method across five major U.S. metropolitan areas by comparing LLM-generated results with existing survey data at three different levels of granularity: (i) pattern level, which compares aggregated metrics like average locations visited and travel time, (ii) trip level, which compares trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of visited places. Our results indicate that fine-tuning an open-weight Llama-3.1 model on a balanced dataset helps approximate age-specific mobility profiles, producing outputs that closely resemble demographic realities. Furthermore, we observe that the model captures implicit seasonal variations in mobility patterns, reproducing fluctuations associated with temperature despite not being given explicit climate inputs. These findings suggest that LLMs, when fine-tuned on as few as 10,000 survey records, can generate synthetic travel data that approximates behavioral realism in major urban contexts. While further validation is needed for non-metropolitan areas and granular transportation metrics, this approach offers a scalable, low-cost, and privacy-preserving complement for urban mobility analysis.