| CAO Jie (曹 洁),ZHANG Pengcheng,ZHANG Hong,HOU Liang,CHEN Zuohan.[J].高技术通讯(英文),2025,31(4):383~396 |
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| Research on traffic flow prediction with multiscale temporal awareness and graph diffusion attention networks |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 04. 008 |
| 中文关键词: |
| 英文关键词: intelligent transportation, traffic flow prediction, graph attention network, multiscale isometric convolution, bi-level routing attention |
| 基金项目: |
| Author Name | Affiliation | | CAO Jie (曹 洁) | (College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, P. R. China) | | ZHANG Pengcheng | | | ZHANG Hong | | | HOU Liang | | | CHEN Zuohan | |
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| 中文摘要: |
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| 英文摘要: |
| Precise traffic flow forecasting is essential for mitigating urban traffic congestion. However, it is difficult for existing methods to adequately capture the dynamic spatio-temporal characteristics and multiscale temporal dependencies of traffic flow. A traffic flow prediction model with multiscale temporal awareness and graph diffusion attention networks (MT-GDAN) is proposed to address these issues. Specifically, a graph diffusion attention module is constructed, which dynamically adjusts and calculates the weights of neighboring nodes in the graph structure using a random graph attention network (GAT) and captures the spatial characteristics of hidden nodes through an adaptive adjacency matrix, thus better exploiting the dynamic spatio-temporal properties of traffic flow. Secondly, a multiscale isometric convolutional network and bi-level routing attention are used to construct a multiscale temporal awareness module. The former extracts local information of traffic flow segments by convolution with different sizes of convolution kernels and then introduces isometric convolution to obtain the global temporal relationship between local features of traffic flow segments; the latter filters irrelevant spatio-temporal features at a coarse regional level and focuses locally on key points to more accurately capture the multiscale temporal dependencies of traffic flows. Experimental results reveal that the MT-GDAN model surpasses the mainstream baseline model in terms of forecasting accuracy and exhibits good prediction performance. |
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