Summary of Traffic Flow Forecasting with Maintenance Downtime Via Multi-channel Attention-based Spatio-temporal Graph Convolutional Networks, by Yuanjie Lu et al.
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
by Yuanjie Lu, Parastoo Kamranfar, David Lattanzi, Amarda Shehu
First submitted to arxiv on: 4 Oct 2021
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel graph-based model is proposed to predict traffic speed under construction work maintenance downtime events. The model integrates various channels of information, including spatial and temporal dependencies, to capture complex relationships between roadway networks. Built upon attention-based spatio-temporal graph convolution architecture, the model outperforms baseline models on benchmark datasets from Tyson’s corner in Northern Virginia. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to predict traffic speed when construction work is happening. This happens by using a special kind of graph that combines different sources of information about traffic and roadways. The graph helps understand how traffic flows change because of construction downtime. This method works better than others on real-world data from Tyson’s corner in Northern Virginia. |
Keywords
* Artificial intelligence * Attention
