Summary of Robust Traffic Forecasting Against Spatial Shift Over Years, by Hongjun Wang et al.
Robust Traffic Forecasting against Spatial Shift over Years
by Hongjun Wang, Jiyuan Chen, Tong Pan, Zheng Dong, Lingyu Zhang, Renhe Jiang, Xuan Song
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (stat.ML)
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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 Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown promising potential for traffic forecasting by capturing temporal and spatial correlations. However, existing datasets lack out-of-distribution (OOD) scenarios, hindering the evaluation of spatiotemporal models’ generalization capacity. In this paper, we propose novel OOD benchmarks and find that state-of-the-art models suffer a significant decline in performance due to their inability to adapt to unobserved spatial relationships. To address this challenge, we introduce a Mixture of Experts (MoE) framework, which learns graph generators during training and combines them to generate new graphs based on novel environmental conditions. Our method is efficient, can be integrated into any spatiotemporal model, and outperforms current state-of-the-art approaches in addressing spatial dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make traffic forecasting models better at handling unexpected situations. Currently, these models are good at predicting what will happen based on past data, but they struggle when faced with new scenarios. The authors propose a new way of making these models more adaptable by combining different graph structures and learning from experience. They test their approach using real-world traffic data and show that it outperforms existing methods in addressing unexpected situations. |
Keywords
» Artificial intelligence » Generalization » Mixture of experts » Spatiotemporal
