Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles

• URL: http://arxiv.org/abs/2407.08047v2
• Date: Mon, 15 Jul 2024 02:31:15 GMT
• Title: Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
• Authors: Jianzhe Xue, Dongcheng Yuan, Yu Sun, Tianqi Zhang, Wenchao Xu, Haibo Zhou, Xuemin, Shen,
• Abstract summary: We introduce a novel framework that utilizes sparse IoV data to achieve cost-effective traffic state estimation (TSE) Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy. 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.
• Score: 23.524936542317842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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