Summary of Synthetic Location Trajectory Generation Using Categorical Diffusion Models, by Simon Dirmeier and Ye Hong and Fernando Perez-cruz
Synthetic location trajectory generation using categorical diffusion models
by Simon Dirmeier, Ye Hong, Fernando Perez-Cruz
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes using diffusion probabilistic models (DPMs) to generate synthetic individual location trajectories (ILTs), which are crucial in mobility research. The authors represent ILTs as multi-dimensional categorical random variables and model their joint distribution using a continuous DPM, first applying the diffusion process in a continuous unconstrained space and then mapping the continuous variables into a discrete space. This approach has the potential to synthesize realistic ILPs, demonstrated by comparing conditionally and unconditionally generated sequences to real-world ILPs from a GNSS tracking data set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computers to create fake maps of where people go. These maps are important for understanding how people move around and making good decisions about cities. The researchers use a special computer program called a diffusion probabilistic model (DPM) to make these fake maps. They take the real maps of where people go and then change them in small ways to make new, different maps that still look like the real ones. This can be helpful for testing other models that try to predict where people will go. |
Keywords
* Artificial intelligence * Diffusion * Probabilistic model * Tracking
