arXiv:2606.05130v1 Announce Type: new Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis. Supervised sequence models achieve strong accuracy but require task-specific training and offer limited decision-level transparency. Recent LLM-based methods improve interpretability, yet mostly rely on static prompts and single-pass inference, limiting their ability to seek additional evidence when mobility signals are weak or conflicting. We propose \method{}, a training-free LLM-driven agent framework that formulates next-location
The proliferation of powerful LLMs and the increasing demand for nuanced urban planning and resource management are driving innovation in AI-driven predictive modeling.
This development allows for more accurate and adaptable individual-level mobility predictions crucial for urban development, transportation efficiency, and resource allocation, with a focus on interpretability.
Mobility prediction shifts from static models to dynamic, evidence-grounded LLM agents, enhancing transparency and adaptability in urban and transportation planning.
- · Urban Planners
- · Transportation Authorities
- · Logistics Companies
- · Smart City Developers
- · Developers of legacy supervised sequence models
- · Static predictive modeling solutions
More efficient and responsive urban infrastructure enabled by better mobility forecasting.
Reduced traffic congestion and improved public transportation utilization in smart cities through dynamic routing and resource allocation.
Enhanced overall quality of life in urban environments due to optimized resource distribution and reduced environmental impact from transportation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG