A Spatiotemporal Coupling Traffic Flow Prediction Model with Dynamic Graph Recursion and State Space

ZHANG Hong; QI Fangzheng; LUO Shengjun; ZHANG Xijun; HOU Liang; HUANG Hairong

doi:10.11999/JEIT251198

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ZHANG Hong, QI Fangzheng, LUO Shengjun, ZHANG Xijun, HOU Liang, HUANG Hairong. A Spatiotemporal Coupling Traffic Flow Prediction Model with Dynamic Graph Recursion and State Space[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251198

Citation:

ZHANG Hong, QI Fangzheng, LUO Shengjun, ZHANG Xijun, HOU Liang, HUANG Hairong. A Spatiotemporal Coupling Traffic Flow Prediction Model with Dynamic Graph Recursion and State Space[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251198

Citation:

ZHANG Hong, QI Fangzheng, LUO Shengjun, ZHANG Xijun, HOU Liang, HUANG Hairong. A Spatiotemporal Coupling Traffic Flow Prediction Model with Dynamic Graph Recursion and State Space[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251198

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A Spatiotemporal Coupling Traffic Flow Prediction Model with Dynamic Graph Recursion and State Space

doi: 10.11999/JEIT251198 cstr: 32379.14.JEIT251198

1.
School of Computer and Artificial Intelligence, Lanzhou University of Technology, Lanzhou 730050, China
2.
School of Computer Science and Engineering, Central South University, Changsha 410017, China

Accepted Date: 2026-04-17
Rev Recd Date: 2026-04-17

Available Online: 2026-04-30

Abstract

Abstract

Objective Accurate traffic flow prediction is crucial for intelligent transportation systems, but it remains challenging due to dynamically evolving spatial dependencies and long-range temporal correlations in urban road networks. To address these issues, this study proposes DGGRU-Mamba, a spatiotemporal traffic forecasting framework that integrates dynamic graph recurrent modeling with a structured state space mechanism and jointly captures adaptive spatial structures and long-term temporal dynamics. Methods The proposed DGGRU-Mamba consists of two core modules: Dynamic Graph Recurrent Modeling (DGRM) and Spatiotemporal Mamba (ST-Mamba). A spatiotemporal embedding generator is introduced to encode periodic temporal information and node-specific spatial features for adaptive graph construction. The DGRM module dynamically updates time-varying adjacency structures through gated graph recurrent units, enabling adaptive modeling of evolving spatial dependencies, while the ST-Mamba module employs structured state transitions to efficiently capture long-range temporal correlations. In addition, a dual-branch prediction scheme, including Forecast and Backcast branches, is adopted to improve multi-step prediction accuracy and alleviate cumulative errors. Results and Discussions DGGRU-Mamba is evaluated on four benchmark datasets, PEMS03, PEMS04, PEMS07, and PEMS08, using MAE, RMSE, and MAPE as evaluation metrics. Experimental results show that the proposed model achieves competitive performance across all datasets. On PEMS04, compared with the mainstream attention-based model STAEformer, DGGRU-Mamba reduces MAE, RMSE, and MAPE by about 4.2%, 3.8%, and 2.9%, respectively, while shortening the inference time by 4.82 s. These results indicate that the proposed framework improves prediction accuracy while maintaining high computational efficiency. The gains mainly stem from the complementary effects of DGRM and ST-Mamba, which enhance dynamic spatial dependency modeling and long-range temporal learning at lower computational cost. Conclusions A novel spatiotemporal traffic flow prediction framework, DGGRU-Mamba, is proposed for modeling dynamic spatial structures and long-term temporal dependencies in complex traffic networks. By integrating dynamic graph recurrent modeling with a structured state space mechanism, the framework achieves a favorable balance between prediction accuracy and computational efficiency. Extensive experiments on multiple benchmark datasets verify its effectiveness and scalability for multi-step traffic forecasting. Future work will consider external factors such as weather and traffic events to further improve practical applicability.
- Traffic flow prediction,
- Dynamic graph modeling,
- state space model,
- Spatiotemporal dependency,
- Multi-step prediction

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References(16)

References

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