Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 205-212.doi: 10.11896/jsjkx.250600133

• Database & Big Data & Data Science • Previous Articles     Next Articles

Traffic Flow Forecasting Based on Dynamic Graph Convolution and Hypergraph Learning

ZHAO Xingbo1, LIAN Defu2   

  1. 1 School of Computer Science,University of Science and Technology of China,Hefei 230000,China
    2 State Key Laboratory of Cognitive Intelligence,University of Science and Technology of China,Hefei 230000,China
  • Received:2025-06-20 Revised:2025-08-11 Online:2026-07-15 Published:2026-07-10
  • About author:ZHAO Xingbo,born in 1999,postgra-duate.His main research interest is spatio-temporal data mining.
    LIAN Defu,born in 1985,professor,Ph.D supervisor.His main research interests include retrieval-augmented large models,large model agents,trustworthy artificial intelligence,recommender systems,and time-series large models.

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Abstract: The traffic flow prediction problem aims to accurately forecast future traffic conditions based on historical observation data.In order to better model the complex spatiotemporal relationships in the traffic network,it is essential to leverage various correlation patterns embedded in the data,including pairwise correlations and higher-order correlations.However,mining these correlation patterns from traffic spatiotemporal data poses significant challenges.Unlike many other domains where prior know-ledge can be used to construct effective correlation structures,traffic data often lacks clear predefined relationships.The dynamic nature of traffic networks further complicates the task,as correlation patterns can change over time due to factors such as weather conditions,accidents,or special events.Therefore,developing methods that can automatically discover and adapt to these changing patterns is crucial for improving the accuracy of traffic flow predictions.In this paper,a novel approach is proposed to address these challenges by utilizing both graph and hypergraph structures.In the pairwise correlation modeling,node embeddings are utilized to infer the graph structure.To better capture the dynamic changes in correlations within the data,a dynamic graph structure learning method is designed to capture correlation patterns from the dynamically changing data.In the higher-order correlation modeling,matrix optimization is employed to directly learn the hypergraph structure,and a contrastive learning loss function is introduced to effectively capture global dependencies.In the temporal dimension,a temporal Transformer architecture is adoptedto model the time-dependent aspects of traffic flow.The self-attention mechanism,a key component of the Transformer,is utilized to achieve multi-scale modeling of temporal dependencies.This allows the model to consider both short-term and long-term patterns in the data,which are essential for accurate traffic flow prediction.Experiments on multiple commonly used traffic flow datasets show that the proposed model can effectively model the complex spatiotemporal relationships in the traffic network,and its prediction accuracy is significantly improved compared to various baseline models.

Key words: Traffic flow forecasting, Dynamic graph convolution, Hypergraph, Spatio-temporal embedding, Graph neural network

CLC Number: 

  • TP183
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