Summary of Multi-scale Traffic Pattern Bank For Cross-city Few-shot Traffic Forecasting, by Zhanyu Liu et al.
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
by Zhanyu Liu, Guanjie Zheng, Yanwei Yu
First submitted to arxiv on: 1 Feb 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 The paper proposes a solution for traffic forecasting, specifically addressing the challenge of limited data in many cities. By recognizing that traffic patterns exhibit similarities across diverse cities, the Multi-scale Traffic Pattern Bank (MTPB) framework is developed. MTPB starts by learning from data-rich source cities through spatial-temporal-aware pre-training and then generates a multi-scale traffic pattern bank using advanced clustering techniques. This bank is used to guide graph reconstruction and forecasting in target cities with limited data. The approach outperforms existing methods on real-world datasets, showcasing its potential for advancing cross-city few-shot forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with predicting traffic flow. Many cities don’t have enough data to make good predictions. But the researchers found that traffic patterns are similar in different cities. They created a system called MTPB that uses this idea to help predict traffic in cities with limited data. MTPB learns from cities with lots of data and then helps other cities make better predictions. The approach works well on real-world data, which is exciting for improving traffic forecasting. |
Keywords
* Artificial intelligence * Clustering * Few shot
