Summary of Spatial-temporal Attention Model For Traffic State Estimation with Sparse Internet Of Vehicles, by Jianzhe Xue et al.
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
by Jianzhe Xue, Dongcheng Yuan, Yu Sun, Tianqi Zhang, Wenchao Xu, Haibo Zhou, Xuemin, Shen
First submitted to arxiv on: 10 Jul 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 A novel framework is introduced for traffic state estimation (TSE) that utilizes sparse internet of vehicles (IoV) data to achieve cost-effective results. The proposed framework, which includes a convolutional retentive network (CRNet), improves TSE accuracy by mining spatial-temporal traffic state correlations. CRNet combines convolutional neural networks (CNNs) for spatial correlation aggregation and retentive networks (RetNets) based on attention mechanisms to extract temporal correlations. The approach is validated through extensive simulations using a real-world IoV dataset, demonstrating its cost-effectiveness and practicality for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic state estimation (TSE) is important for intelligent transportation systems (ITS). A new way to do this using only some of the internet of vehicles (IoV) data is being proposed. This approach can avoid big problems with collecting and processing lots of data. The idea uses a special kind of computer model called a convolutional retentive network (CRNet) that helps make TSE more accurate by looking at how traffic patterns change over time and space. |
Keywords
* Artificial intelligence * Attention
