Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems
MR-Traj is a novel multi-resolution diffusion framework for large-scale synthetic trajectory generation in urban systems It models trajectories as compositions of coarse-grained milestones and fine-grained segments, capturing spatial-temporal dependencies across multiple resolutions MR-Traj matches state-of-the-art methods in global distribution similarity while outperforming them in fine-resolution mobility pattern modeling and downstream urban tasks Introducing stochasticity at multiple resolu
Analysis
TL;DR
- MR-Traj is a novel multi-resolution diffusion framework for large-scale synthetic trajectory generation in urban systems
- It models trajectories as compositions of coarse-grained milestones and fine-grained segments, capturing spatial-temporal dependencies across multiple resolutions
- MR-Traj matches state-of-the-art methods in global distribution similarity while outperforming them in fine-resolution mobility pattern modeling and downstream urban tasks
- Introducing stochasticity at multiple resolution levels increases trajectory diversity and empirically reduces trajectory linkage risk under seed-guided data release
Why It Matters
This work addresses a critical gap in urban AI: the tension between privacy concerns and the need for large-scale trajectory data. By enabling high-quality synthetic trajectory generation with built-in privacy protections, MR-Traj can accelerate research and deployment in traffic management, epidemic control, and urban planning without exposing sensitive real-world movement data.
Technical Details
- Architecture: Multi-resolution diffusion model that decomposes trajectories into coarse-grained milestones (major waypoints) and fine-grained segments (detailed paths between milestones), processed at different resolution levels
- Methodology: Explicitly models spatial-temporal dependencies across resolutions rather than treating all trajectory points uniformly, allowing the model to capture both macro-level movement patterns and micro-level behavioral details
- Privacy mechanism: Stochasticity injected at multiple resolution levels increases output diversity, which empirically reduces re-identification/linkage risk in seed-guided data release scenarios
- Evaluation: Benchmarked against state-of-the-art synthetic trajectory generation methods on global distribution similarity metrics and fine-resolution mobility pattern fidelity, with demonstrations on downstream urban mobility tasks
Industry Insight
- Urban data platforms and smart city initiatives can leverage MR-Traj to generate privacy-preserving synthetic datasets, enabling broader data sharing while mitigating re-identification risks
- The multi-resolution approach could be adapted beyond urban trajectories to other spatiotemporal domains such as logistics, wildlife tracking, and transportation network modeling
- The seed-guided data release framework with built-in stochasticity offers a practical blueprint for organizations looking to publish trajectory data while maintaining compliance with privacy regulations
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