Summary of Xxltraffic: Expanding and Extremely Long Traffic Dataset For Ultra-dynamic Forecasting Challenges, by Du Yin et al.
XXLTraffic: Expanding and Extremely Long Traffic Dataset for Ultra-Dynamic Forecasting Challenges
by Du Yin, Hao Xue, Arian Prabowo, Shuang Ao, Flora Salim
First submitted to arxiv on: 18 Jun 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 introduces XXLTraffic, the largest public traffic dataset with a long timespan and increasing sensor nodes over multiple years. This dataset aims to bridge the gap between current datasets and real-world scenarios, which are characterized by dynamic infrastructures, varying temporal distributions, and gaps due to sensor downtimes or changes in traffic patterns. The benchmark includes typical time-series forecasting settings as well as novel configurations introducing gaps and downsampling training size to simulate practical constraints. This dataset is expected to provide a fresh perspective for the time-series and traffic forecasting communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a big difference by creating a new, super-big traffic data set that’s really good at showing how traffic changes over time. Right now, there aren’t many datasets like this one, so it will help researchers make better predictions about what might happen in the future. This dataset is special because it has a lot of sensors and can show how traffic changes in different ways. It’s like a puzzle that needs to be solved! |
Keywords
* Artificial intelligence * Time series
