Traffic Flow Prediction Based on a Dual-channel Spatio-temporal Graph Neural Network

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Traffic Flow Prediction Based on a Dual-channel Spatio-temporal Graph Neural Network

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[1]ZHANG Zhen,LI Zhuo,LIU Bo,et al.Traffic Flow Prediction Based on a Dual-channel Spatio-temporal Graph Neural Network[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10.13705/j.issn.1671-6833.2026.02.004]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-9 Column: Public date: 2027-12-10

Title:: Traffic Flow Prediction Based on a Dual-channel Spatio-temporal Graph Neural Network

Author(s):: ZHANG Zhen ¹ , LI Zhuo ¹ , LIU Bo ² , MA Jj ³ , KONG Lt ³ , WANG Za³; 1. School of Electrical and Information Engineering, Zhengzhou University , Zhengzhou 450001, China; 2. School of Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China; 3. Henan Provincial Department of transportation, Zhengzhou 450016, China

Keywords:: traffic flow forecasting ; frequency-enhanced mechanism; discrete wavelet transform; multi-order graph convolution; spatio-temporal graph neural network

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2026.02.004

Abstract:: To overcome the limitations of existing traffic flow forecasting methods, which often fail to adequately capture heterogeneous temporal patterns and struggle to accurately model the time-varying characteristics of dynamic spatial dependencies, this study proposes a Dual-Channel Spatio-Temporal Graph Neural Network (DC-STGNN). In the temporal dimension, a decoupling layer based on discrete wavelet transform is designed to decompose traffic sequences into low-frequency and high-frequency components. The low-frequency components are modeled using a frequency-enhanced module, while the high-frequency components were captured through multi-scale dilated causal convolution, enabling precise modeling of heterogeneous temporal patterns. In addition, a temporal gating mechanism is introduced to dynamically balance the contributions of long-term trends and short-term fluctuations according to traffic variations, thereby achieving more targeted temporal feature extraction and improving adaptability across different time scales. In the spatial dimension, multi-order diffusion graph convolution and multi-head graph attention are integrated to separately extract static structural features and dynamic spatial dependencies of the traffic network, effectively capturing the evolving spatial interaction patterns in complex traffic systems. Furthermore, a spatial gating mechanism was developed to adaptively fuse static and dynamic graph information, enhancing the model’s capability in representing complex spatial structures. Extensive experiments on real-world traffic datasets--PEMS-04, PEMS-08, PEMS-BAY, and METR-LA--for 15-, 30-, and 60-minute traffic flow prediction tasks show that DC-STGNN achieves higher prediction accuracy and better long-term stability compared to the best-performing baseline models.

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