MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

MobiDiff is an end-to-end discrete diffusion framework designed for generating synthetic human mobility data, addressing privacy concerns and high collection costs of real-world data. Unlike prior diffusion methods that rely on continuous or latent spatio-temporal traces, MobiDiff directly models discrete semantic events by decomposing each check-in into spatial, activity, and temporal channels. It employs structured event-, group-, and channel-level masking to capture trajectory-level patterns and within-event dependencies. Evaluated on large-scale datasets from Atlanta, Boston, and Seattle, MobiDiff achieves 5.3x faster inference than GeoGen and significantly improves temporal and selected semantic fidelity, with an average JSD of 0.084 compared to GeoGen's 0.216. While showing competitive utility for downstream tasks and lower empirical exposure risk than MoveSim, it uses more GPU memory (1168 MB) and its spatial fidelity can be improved.

Key takeaway

For Machine Learning Engineers developing synthetic human mobility data generators, MobiDiff offers a compelling alternative to continuous diffusion models. You should consider its discrete diffusion approach to achieve significantly faster inference (5.3x over GeoGen) and superior temporal/semantic fidelity. While its GPU memory footprint is higher and spatial fidelity can vary, its ability to preserve fine-grained POI signals and reduce empirical exposure risk makes it valuable for augmenting limited real datasets or replacing them in certain downstream tasks.

Key insights

Discrete diffusion directly models semantic mobility events, offering efficient and interpretable synthetic data generation.

Principles

• Discrete diffusion suits categorical mobility states.
• Multi-channel events capture spatial, activity, temporal factors.
• Numeric-aware embeddings preserve geographic and temporal structure.

Method

MobiDiff tokenizes trajectories into multi-channel semantic skeletons, applies structured masked diffusion, and uses a numeric-aware bidirectional Transformer denoiser with auxiliary coordinate and time objectives for reconstruction.

In practice

• Model mobility as variable-length discrete semantic events.
• Employ structured masking for event, group, and channel levels.

Topics

• Human Mobility Data
• Discrete Diffusion Models
• Synthetic Data Generation
• Spatio-Temporal Data
• Generative AI
• Masked Denoising

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.