MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
MobiDiff uses multi-channel discrete diffusion to efficiently generate privacy-preserving human mobility data.
Key Findings
Methodology
MobiDiff is an end-to-end discrete diffusion framework that generates mobility data by directly denoising multi-channel semantic skeletons. It decomposes each check-in event into spatial, activity, and temporal channels and employs structured event-, group-, and channel-level masking to capture trajectory-level mobility patterns and within-event dependencies.
Key Results
- On datasets from Atlanta, Boston, and Seattle, MobiDiff excels in preserving trajectory length and temporal interval distributions, being 5.3 times faster than GeoGen during inference.
- MobiDiff achieves low JSD between generated and real data distributions, indicating high fidelity of the generated trajectories.
- The utility of synthetic data is validated in downstream prediction tasks, showing efficiency in macro-region, POI, and activity-category prediction.
Significance
MobiDiff offers an interpretable and efficient framework for synthetic mobility data generation, with significant advantages in privacy preservation and generation speed. It addresses the limitations of existing methods in modeling discrete semantic events, opening new possibilities for transportation optimization, urban planning, and resource allocation.
Technical Contribution
MobiDiff introduces a multi-channel discrete diffusion model, avoiding interpolation and coarse-to-fine realization processes of continuous traces. It directly handles discrete semantic events, providing new engineering possibilities and theoretical guarantees.
Novelty
MobiDiff is the first to apply discrete diffusion to human mobility data generation, directly handling discrete semantic events instead of relying on continuous or latent spatiotemporal traces, significantly improving generation efficiency and data fidelity.
Limitations
- Spatial fidelity still leaves room for improvement on some city-metric combinations, potentially affecting accuracy in certain applications.
- Performance may degrade when handling very long trajectories.
Future Work
Future work can explore improving spatial fidelity and applying this method to a wider range of cities and scenarios. Additionally, maintaining efficiency on longer trajectories is an important direction.
AI Executive Summary
MobiDiff is an innovative semantic-aware multi-channel discrete diffusion framework for generating synthetic human mobility data, addressing the shortcomings of existing methods in modeling discrete semantic events. Existing diffusion methods typically rely on continuous or latent spatiotemporal traces, failing to effectively capture discrete semantic events. MobiDiff avoids costly interpolation and coarse-to-fine realization processes by directly denoising multi-channel semantic skeletons.
In experiments, MobiDiff was evaluated on three large-scale datasets from Atlanta, Boston, and Seattle, showing excellent performance in preserving trajectory length and temporal interval distributions while remaining competitive across broader mobility statistics. It is 5.3 times faster on average during inference compared to state-of-the-art methods.
The technical contribution of MobiDiff lies in its innovative multi-channel discrete diffusion model, providing an interpretable and efficient framework for synthetic mobility data generation. Future research can further improve spatial fidelity and explore the potential of this method in broader application scenarios.
Deep Analysis
Background
Human mobility data play a crucial role in transportation optimization, urban planning, and resource allocation. However, real mobility data are difficult to collect and share at scale due to privacy concerns and high collection costs. Synthetic data generation has thus become an attractive way to support large-scale data access. Recent advances in generative modeling have enabled synthetic mobility generation through diverse model families, including GAN-based, LLM-based, and diffusion-based methods.
Core Problem
Existing diffusion methods for generating synthetic mobility data typically rely on continuous or latent spatiotemporal traces, limiting their ability to effectively model discrete semantic events with explicit region, activity, time, and interval structures. This limits their native modeling capability for discrete semantic events during generation.
Innovation
MobiDiff avoids costly interpolation and coarse-to-fine realization processes by directly denoising multi-channel semantic skeletons. It decomposes each check-in event into spatial, activity, and temporal channels and employs structured event-, group-, and channel-level masking to capture trajectory-level mobility patterns and within-event dependencies.
Methodology
- �� MobiDiff decomposes each check-in event into spatial, activity, and temporal channels.
- �� Employs structured event-, group-, and channel-level masking.
- �� Directly denoises multi-channel semantic skeletons using a multi-channel discrete diffusion model.
- �� Avoids costly interpolation and coarse-to-fine realization processes.
Experiments
Experiments were conducted on three large-scale datasets from Atlanta, Boston, and Seattle, evaluating the fidelity of generated mobility data in terms of trajectory length, temporal interval distributions, and broader mobility statistics. MobiDiff is 5.3 times faster on average during inference compared to state-of-the-art methods.
Results
MobiDiff excels in preserving trajectory length and temporal interval distributions while remaining competitive across broader mobility statistics. It is 5.3 times faster on average during inference compared to state-of-the-art methods.
Applications
MobiDiff can be directly applied to scenarios such as transportation optimization, urban planning, and resource allocation. The synthetic data generated can support large-scale data access while preserving user privacy.
Limitations & Outlook
Spatial fidelity still leaves room for improvement on some city-metric combinations. Performance may degrade when handling very long trajectories. Future work can explore improving spatial fidelity and applying this method to a wider range of cities and scenarios.
Plain Language Accessible to non-experts
Imagine traveling in a big city. MobiDiff is like a smart travel guide that generates a new travel plan based on your past travel habits. This plan considers not only the places you've been but also the times and activities at those places. This way, you can enjoy a new travel experience without revealing personal privacy.
ELI14 Explained like you're 14
Imagine you're playing a city simulation game. MobiDiff is like a super assistant in the game that helps you generate people's movement routes in the city. This assistant knows not only where people go but also their activities and times there. This way, you can better plan the city's development without worrying about invading people's privacy.
Glossary
MobiDiff
A multi-channel discrete diffusion framework for generating synthetic mobility data.
Used to directly denoise multi-channel semantic skeletons.
Semantic Skeleton
A multi-channel structure composed of spatial, activity, and temporal channels.
Used to represent each check-in event.
Discrete Diffusion
A diffusion process defined over discrete states.
Used for generating synthetic mobility data.
Jensen–Shannon Divergence (JSD)
A metric for measuring the similarity between two probability distributions.
Used to evaluate the distributional similarity between generated and real data.
GeoGen
A two-stage generator for fine-grained human mobility trajectories.
One of the baseline methods compared with MobiDiff.
Open Questions Unanswered questions from this research
- 1 How to improve MobiDiff's spatial fidelity on different city-metric combinations?
- 2 How to maintain MobiDiff's efficiency on longer trajectories?
Applications
Immediate Applications
Traffic Optimization
Synthetic data generated by MobiDiff can be used for traffic flow prediction and optimization. Traffic management departments can use this data to improve traffic signals and route planning.
Long-term Vision
Urban Planning
With the generated synthetic data, urban planners can better understand human flow patterns in the city, leading to more effective urban development planning.
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 end-to-end discrete diffusion framework that efficiently generates mobility data by directly denoising multi-channel semantic skeletons, avoiding the costly interpolation, latent trace construction, and coarse-to-fine realization pipelines widely used in existing diffusion-based methods. Specifically, MobiDiff decomposes each human check-in event into spatial, activity, and temporal channels, and employs structured event-, group-, and channel-level masking to jointly capture trajectory-level mobility patterns and within-event dependencies. We evaluate generation fidelity, privacy-preserving, and efficiency on three large-scale real-world datasets from Atlanta, Boston, and Seattle. Results show that MobiDiff effectively preserves trajectory length and temporal interval distributions while remaining competitive across broader mobility statistics; it is also much faster than state-of-the-art methods, e.g., 5.3$\times$ faster than GeoGen on average during inference. These findings suggest that discrete diffusion offers an interpretable and efficient framework for synthetic mobility data generation.