Summary of Bigcity: a Universal Spatiotemporal Model For Unified Trajectory and Traffic State Data Analysis, by Xie Yu et al.
BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis
by Xie Yu, Jingyuan Wang, Yifan Yang, Qian Huang, Ke Qu
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 introduces BIGCity, a novel multi-task, multi-data modality (MTMD) model for spatiotemporal (ST) data analysis. Traditional approaches treat trajectory and traffic state data as distinct modalities, but this limits model performance in real-world applications like navigation apps. The MTMD approach addresses this gap by unifying the representations of different ST data modalities and heterogeneous tasks. BIGCity includes an ST-unit for unified representation of trajectories and traffic states and a tunable large model with task-oriented prompts. Experimental results on real-world datasets demonstrate state-of-the-art performance across 8 tasks, outperforming 18 baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BIGCity is a new way to analyze data about how people move around cities. Right now, most models can only look at one type of data or the other, like where individual people are moving or what the overall traffic flow looks like. But in real life, we need to use both types of data together. This paper creates a model that can do just that. It’s called BIGCity and it uses a special way of combining data to make better predictions. The results show that BIGCity is much better than other models at doing this. |
Keywords
» Artificial intelligence » Multi task » Spatiotemporal
