Summary of Deep Activity Model: a Generative Approach For Human Mobility Pattern Synthesis, by Xishun Liao et al.
Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis
by Xishun Liao, Qinhua Jiang, Brian Yueshuai He, Yifan Liu, Chenchen Kuai, Jiaqi Ma
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel generative deep learning approach for human mobility modeling and synthesis is developed to address the limitations of existing models. The model incorporates both activity patterns and location trajectories using open-source data and can be fine-tuned with local data to adapt to diverse regions. It demonstrates superior performance in generating activity-location chains that closely follow ground truth distributions on a nationwide dataset of the United States, and shows transferability in state- or city-specific datasets from California, Washington, and Mexico City. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how people move around is being developed using deep learning. This approach combines information about what people are doing (like work or shopping) with where they are going. It can be used to create fake data that shows how people might move around in different places. The model does a great job of matching real patterns and works well in different parts of the United States and other countries. |
Keywords
» Artificial intelligence » Deep learning » Transferability
