MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
arXiv:2607.08357v1 Announce Type: new Abstract: Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns. Recent diffusion-based methods have shown promise in synthesizing realistic mobility patterns, but they typically rely on continuous or latent spatio-temporal traces, limiting their ability to natively model discrete semantic events with explicit region, activity, time, and interval structures. To address this issue, we introduce MobiDiff, an
The increasing sophistication of diffusion models combined with growing privacy concerns and the high cost of real-world data collection make synthetic data generation for human mobility an immediate need.
Generating realistic synthetic human mobility data can drastically reduce data collection costs, mitigate privacy risks, and accelerate advancements in urban planning, transportation, and resource allocation without relying on sensitive real-world datasets.
The ability to produce high-fidelity synthetic mobility data that models discrete semantic events (region, activity, time, interval) opens new avenues for simulating complex societal behaviors and infrastructure interactions.
- · Urban planners
- · Transportation optimization software firms
- · AI model developers
- · Researchers in social sciences
- · Traditional mobility data providers
- · Organizations reliant on proprietary, costly real-world datasets
- · Low-fidelity simulation techniques
More accurate and privacy-preserving simulations become possible for city development and logistics.
Demand for privacy-preserving synthetic data generation techniques increases across various sensitive domains beyond mobility.
The proliferation of advanced synthetic data could lead to new forms of algorithmic bias if not carefully managed during model training.
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Read at arXiv cs.AI