Summary of Continual Traffic Forecasting Via Mixture Of Experts, by Sanghyun Lee et al.
Continual Traffic Forecasting via Mixture of Experts
by Sanghyun Lee, Chanyoung Park
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed Traffic Forecasting Mixture of Experts (TFMoE) model addresses the challenges of incremental training on evolving traffic networks. Traditional methods suffer from catastrophic forgetting, where past knowledge is lost when new sensors are added. TFMoE segments the traffic flow into groups and assigns an expert model to each, allowing them to adapt to specific patterns while preserving prior knowledge. The approach demonstrates superior performance and resilience in real-world datasets like PEMSD3-Stream, showcasing its effectiveness in dealing with continual learning for traffic flow forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict traffic patterns when the network changes over time. Right now, we train models on all the sensors, which is not efficient or effective. The proposed model, TFMoE, groups similar traffic patterns together and assigns an expert model to each group. This helps the model learn from the past while adapting to new information. The result is a more accurate and reliable prediction of traffic flow in real-world networks. |
Keywords
» Artificial intelligence » Continual learning » Mixture of experts
