Open Access

ARTICLE

Spatial-Temporal Graph Fusion with Dual-Scale Convolution for Traffic Flow Prediction

Dan Wang¹, Mengyi Cui¹, Zhenhua Yu^1,*, Yukang Liu²

1 College of Artificial Intelligence & Computer Science, Xi’an University of Science and Technology, Xi’an, China
2 China Petroleum Engineering & Construction Corp. Beijing Company, Beijing, China

* Corresponding Author: Zhenhua Yu. Email:

Computers, Materials & Continua 2026, 87(3), 54 https://doi.org/10.32604/cmc.2026.074308

Received 08 October 2025; Accepted 23 December 2025; Issue published 09 April 2026

Abstract

Traffic flow prediction is of great importance in traffic planning, road resource management, and congestion mitigation. However, existing prediction have significant limitations in modeling multi-scale spatial-temporal features, particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks. This paper proposes a novel framework of traffic flow prediction, referred to as Adaptive Graph Fusion Dual-scale Convolutional Network (AGFDCN), which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks. Specifically, we introduce a Dual-Scale Temporal Network, which combines long- and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales. Furthermore, we design a Dynamic Adaptive Graph Module, which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module. Finally, the temporal and spatial representations are integrated by employing a gated fusion mechanism, enhancing the overall prediction performance. Experimental results obtained based on three highway datasets (i.e., PEMS04, PEMS07 and PEMS08) verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics. Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models, the proposed model exhibits significant improvements across all datasets: it achieves reduces of MAE by 42.07% and RMSE by 35.43% on PEMS04; MAE by 28.35% and RMSE by 29.28% on PEMS07; and MAE by 30.52% and RMSE by 30.73% on PEMS08, respectively, validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes.

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Keywords

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