arXiv:2605.30014v1 Announce Type: new Abstract: Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict access to large-scale and high-quality trajectory datasets. Trajectory generation provides a promising alternative by synthesizing realistic data to mitigate privacy risks. However, existing methods fail to explicitly capture travel patterns and can only generate fixed-length trajectories under a single condition. To address these limitations, we propose \textbf{HTP}, which \textbf{H}ierarchically ge
The increasing focus on privacy and the limitations of existing trajectory generation methods are driving innovation in synthetic data creation, particularly with the advancements in LLMs.
This development addresses a critical need for realistic urban trajectory data for smart city applications while mitigating privacy risks, enabling new research and development previously constrained by data scarcity.
The ability to generate flexible and semantic trajectory data using LLMs will enhance urban modeling, transportation planning, and city management with synthetic, privacy-preserving datasets.
- · Smart City Developers
- · Urban Planners
- · AI/ML Researchers
- · Data Privacy Solutions
- · Traditional Data Collection Methods
- · Companies reliant on sensitive user data for urban insights
Improved simulation and predictive models for urban dynamics become possible without real-world data constraints.
New privacy-preserving urban applications and services emerge, fostering innovation in smart cities.
The broader adoption of synthetic data generation could reduce the collection of sensitive real-world datasets, impacting existing data-reliant business models.
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Read at arXiv cs.AI