收藏切换
Traffic flow prediction model based on adaptive spatio-temporal multi-head graph attention network
收藏切换
PDF
Xinmin ZHOU1, 2, Tian XU3, Da LI3, Longxin WANG4, Jianghua HU3, Wei WANG4
China Safety Science Journal | 2025, 35(11) : 149 - 156
Less
收藏切换
China Safety Science Journal | 2025, 35(11): 149-156
Public safety
Traffic flow prediction model based on adaptive spatio-temporal multi-head graph attention network
Full
Xinmin ZHOU1, 2, Tian XU3, Da LI3, Longxin WANG4, Jianghua HU3, Wei WANG4
Affiliations
  • 1School of Artificial Intelligence and Advanced Computing, Hunan University of Technology and Business, Changsha Hunan 410205, China
  • 2Xiangjiang Laboratory, Changsha Hunan 410205, China
  • 3School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha Hunan 410205, China
  • 4School of Computer Science, Hunan University of Technology and Business, Changsha Hunan 410205, China
Published: 2025-11-28 doi: 10.16265/j.cnki.issn1003-3033.2025.11.0469
Outline
收藏切换

An adaptive spatio-temporal multi-head graph attention network was proposed for traffic-flow prediction to improve urban traffic safety and alleviate congestion. A three-stage architecture was designed to achieve refined prediction. Firstly, a spatio-temporal information from traffic flow data was encoded by a spatio-temporal encoding and a traffic-flow graph was constructed. Subsequently, the traffic-flow graph is adaptively enhanced at both the flow attribute and graph structure levels. Then, a two-branch multi-head spatial self-attention mechanism was employed to mine intrinsic associations at global and local spatial scales, thereby decoupling complex spatial dependencies. By temporal heterogeneity and a multi-head temporal attention mechanism, the complex dynamic relationships in time-series data were capture from multiple perspectives. A multilayer perceptron (MLP) was incorporated to explore deep connections between present and future traffic flows. The model's effectiveness was validated using real-world datasets. The results demonstrate that the proposed model outperforms existing methods in key metrics for traffic flow prediction, with reductions in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of up to 5.12% and 19.11%, respectively. Furthermore, a high degree of agreement was observed between the predicted and true values, particularly in scenarios with sudden traffic fluctuations. The predictions reflect the actual changes effectively. By optimizing spatio-temporal feature extraction capabilities, prediction errors are effectively reduced and the performance of traffic flow prediction is improved, which demonstrates the effectiveness of the model.

spatio temporal multi head graph attention network  /  adaptive  /  multi-head spatial self-attention  /  heterogeneity  /  spatio-temporal data
Xinmin ZHOU, Tian XU, Da LI, Longxin WANG, Jianghua HU, Wei WANG. Traffic flow prediction model based on adaptive spatio-temporal multi-head graph attention network[J]. China Safety Science Journal, 2025 , 35 (11) : 149 -156 . DOI: 10.16265/j.cnki.issn1003-3033.2025.11.0469
Year 2025 volume 35 Issue 11
PDF
46
8
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.11.0469
  • Receive Date:2025-07-12
  • Online Date:2026-07-09
  • Published:2025-11-28
Article Data
Affiliations
History
  • Received:2025-07-12
  • Revised:2025-09-16
Funding
Affiliations
    1School of Artificial Intelligence and Advanced Computing, Hunan University of Technology and Business, Changsha Hunan 410205, China
    2Xiangjiang Laboratory, Changsha Hunan 410205, China
    3School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha Hunan 410205, China
    4School of Computer Science, Hunan University of Technology and Business, Changsha Hunan 410205, China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2025.11.0469
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT