Do LLMs Know Your Neighborhood? Auditing LLM Priors for Neighborhood-Level Mobility Prediction and Structural Alignment

Research is evaluating the capability of Large Language Models (LLMs) to predict neighborhood-level human mobility, which is crucial for urban planning, transportation, public health, and emergency response. This study investigates whether zero-shot LLMs can infer aggregate mobility patterns at the Census Block Group level across U.S. metropolitan areas, comparing their performance against supervised baselines. This addresses the challenge of accessing fine-grained, privacy-sensitive trajectory data by exploring LLMs as a potential alternative.

This research addresses a critical need for accessible, granular human mobility data, which is foundational for effective urban infrastructure development, public safety, and resource allocation. If LLMs prove capable, they could offer a scalable and less resource-intensive alternative to proprietary data, significantly enhancing strategic planning and operational responsiveness across numerous public and private sectors.

Human mobility data is vital for critical sectors like urban planning, transportation, public health, and emergency response.

Forward consideration, not a verified fact.