Summary of Cola: Cross-city Mobility Transformer For Human Trajectory Simulation, by Yu Wang et al.
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation
by Yu Wang, Tongya Zheng, Yuxuan Liang, Shunyu Liu, Mingli Song
First submitted to arxiv on: 4 Mar 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 In this paper, the authors aim to overcome the limitations of existing deep learning models for human trajectory simulation by exploring mobility transfer across cities. They develop a Cross-city mObiLity trAnsformer (COLA) that combines private city-specific modules with shared universal mobility patterns. COLA leverages a post-hoc adjustment strategy to simulate trajectories without compromising model-agnostic knowledge transfer. The authors demonstrate the superiority of COLA compared to state-of-the-art single-city baselines and cross-city baselines through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to use data from daily mobile devices to plan cities, prevent epidemics, and more. The researchers created a new tool called Cross-city mObiLity trAnsformer (COLA) that can take information from one city and apply it to another city. This is important because we don’t have enough data in many places, making it hard for computers to learn what human behavior looks like. The authors tested COLA and found it works better than other methods. |
Keywords
* Artificial intelligence * Deep learning * Transformer
