arXiv:2606.12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation. Existing LLM-based generators typically rely on either prompt engineering, which preserves zero-shot reasoning but lacks fine-grained spatiotemporal grounding, or trajectory-level fine-tuning, which improves statistical precision but incurs substantial computational cost and may weaken general reasoning. We propose TrajGenAgen
The increasing sophistication of LLMs and the growing demand for large-scale, privacy-preserving synthetic data across various sectors are converging, making this advancement timely.
This development allows for the generation of realistic human mobility data at scale, bypassing traditional collection costs and privacy issues, which is crucial for urban planning, transportation optimization, and public health modeling.
The ability to generate high-fidelity synthetic human mobility data using hierarchical LLM agents reduces reliance on sensitive, real-world data, enabling more rapid and ethical development in data-intensive fields.
- · Urban Planners
- · Transportation Logisticians
- · Epidemiologists
- · AI Data Providers
- · Traditional Data Collection Services
- · Privacy-constrained Data Analytics Firms (without synthetic alternatives)
More efficient and data-rich simulations for urban infrastructure and public health interventions can be developed without individual privacy breaches.
Demand for synthetic data generation models will increase, leading to a new sub-sector focused on specialized generative AI for various data types.
The widespread availability of high-quality synthetic mobility data could influence real-world population distribution and infrastructure development by optimizing for predicted movement patterns.
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Read at arXiv cs.AI