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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08047 |
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| _version_ | 1866911954789990400 |
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| author | Xue, Jianzhe Yuan, Dongcheng Sun, Yu Zhang, Tianqi Xu, Wenchao Zhou, Haibo Xuemin Shen |
| author_facet | Xue, Jianzhe Yuan, Dongcheng Sun, Yu Zhang, Tianqi Xu, Wenchao Zhou, Haibo Xuemin Shen |
| contents | The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS). By utilizing only a portion of IoV data instead of the entire dataset, the significant overheads associated with collecting and processing large amounts of data can be avoided. In this paper, we introduce a novel framework that utilizes sparse IoV data to achieve cost-effective TSE. Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy by mining spatial-temporal traffic state correlations. The model employs the convolutional neural network (CNN) for spatial correlation aggregation and the retentive network (RetNet) based on the attention mechanism to extract temporal correlations. Extensive simulations on a real-world IoV dataset validate the advantage of the proposed TSE approach in achieving accurate TSE using sparse IoV data, demonstrating its cost effectiveness and practicality for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08047 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles Xue, Jianzhe Yuan, Dongcheng Sun, Yu Zhang, Tianqi Xu, Wenchao Zhou, Haibo Xuemin Shen Machine Learning Artificial Intelligence The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS). By utilizing only a portion of IoV data instead of the entire dataset, the significant overheads associated with collecting and processing large amounts of data can be avoided. In this paper, we introduce a novel framework that utilizes sparse IoV data to achieve cost-effective TSE. Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy by mining spatial-temporal traffic state correlations. The model employs the convolutional neural network (CNN) for spatial correlation aggregation and the retentive network (RetNet) based on the attention mechanism to extract temporal correlations. Extensive simulations on a real-world IoV dataset validate the advantage of the proposed TSE approach in achieving accurate TSE using sparse IoV data, demonstrating its cost effectiveness and practicality for real-world applications. |
| title | Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.08047 |