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

Jul 10, 2026

Researchers introduce MobiDiff, an end-to-end discrete diffusion framework for generating synthetic human mobility data by denoising multi-channel semantic skeletons. The method decomposes check-in events into spatial, activity, and temporal channels, using structured masking to capture trajectory patterns. Evaluated on three real-world datasets, MobiDiff preserves key mobility statistics and is 5.3 times faster than GeoGen during inference.

Why it matters: MobiDiff provides an interpretable and efficient approach to generating realistic mobility data, which can help address privacy and data collection challenges in transportation and urban planning.

Full story at: arXiv AI/ML ↗