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

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

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

PDF( 1710 KB)

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

Received Date: 2025-11-13
Accepted Date: 2026-04-17
Rev Recd Date: 2026-04-17

Available Online: 2026-04-30

Abstract

Abstract

Objective Accurate traffic flow prediction is a key task in intelligent transportation systems. However, it remains challenging to capture dynamically evolving spatial structures and complex spatiotemporal dependencies in urban road networks. To address these issues, this paper proposes DGGRU-Mamba, a spatiotemporal traffic flow prediction framework that integrates dynamic graph recurrent modeling with a structured state space mechanism. The model jointly captures dynamic spatial dependencies and long-range temporal dependencies. Methods DGGRU-Mamba contains two core modules: Dynamic Graph Recurrent Modeling (DGRM) and Spatiotemporal Mamba (ST-Mamba). A spatiotemporal embedding generator is first designed to jointly encode periodic temporal information and node-specific spatial features, thereby supporting adaptive graph construction. DGRM dynamically updates time-varying adjacency structures through Dynamic Graph Gated Recurrent Units (DGGRUs), which enables adaptive modeling of evolving spatial dependencies. ST-Mamba uses structured state transitions to efficiently capture long-range temporal dependencies. In addition, a dual-branch prediction scheme with Forecast and Backcast branches is used to improve multi-step prediction accuracy and reduce cumulative errors. Results and Discussions DGGRU-Mamba is evaluated on four benchmark datasets, namely PEMS03, PEMS04, PEMS07, and PEMS08. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used as evaluation metrics. Experimental results show that DGGRU-Mamba achieves strong performance on all datasets. On PEMS04, compared with the mainstream attention-based model STAEformer, DGGRU-Mamba reduces MAE, RMSE, and MAPE by approximately 4.2%, 3.8%, and 2.9%, respectively. Its inference time is also shortened by 4.82 s. These results indicate that the proposed framework improves prediction accuracy while maintaining high computational efficiency. The performance gains mainly arise from the complementary effects of DGRM and ST-Mamba, which strengthen dynamic spatial dependency modeling and long-range temporal dependency learning with lower computational cost. Conclusions This paper proposes DGGRU-Mamba, a spatiotemporal traffic flow prediction framework for modeling dynamic spatial structures and long-range 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. Experiments on multiple benchmark datasets verify its effectiveness and scalability in multi-step traffic flow prediction. Future work will consider external factors, such as weather and traffic events, to further improve its applicability in real traffic scenarios.
- Traffic flow prediction,
- Dynamic graph gated recurrence,
- state space model,
- Spatiotemporal dependencies,
- Multi-step prediction

FullText(HTML)

References(16)

References

[1]	张红, 伊敏, 张玺君, 等. 长期Transformer和自适应傅里叶变换的动态图卷积交通流预测研究[J]. 电子与信息学报, 2025, 47(7): 2249–2262. doi: 10.11999/JEIT241076. ZHANG Hong, YI Min, ZHANG Xijun, et al. Long-term Transformer and adaptive Fourier transform for dynamic graph convolutional traffic flow prediction study[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2249–2262. doi: 10.11999/JEIT241076.
[2]	智慧, 段苗苗, 杨利霞, 等. 一种基于区块链和联邦学习融合的交通流预测方法[J]. 电子与信息学报, 2024, 46(9): 3777–3787. doi: 10.11999/JEIT240030. ZHI Hui, DUAN Miaomiao, YANG Lixia, et al. A traffic flow prediction method based on the fusion of blockchain and federated learning[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3777–3787. doi: 10.11999/JEIT240030.
[3]	殷礼胜, 刘攀, 孙双晨, 等. 基于互补集合经验模态分解和改进麻雀搜索算法优化双向门控循环单元的交通流组合预测模型[J]. 电子与信息学报, 2023, 45(12): 4499–4508. doi: 10.11999/JEIT221172. YIN Lisheng, LIU Pan, SUN Shuangchen, et al. Traffic flow combined prediction model based on complementary ensemble empirical mode decomposition and bidirectional gated recurrent unit optimized by improved sparrow search algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4499–4508. doi: 10.11999/JEIT221172.
[4]	ZHANG Qingyong, LI Changwu, SU Fuwen, et al. Spatiotemporal residual graph attention network for traffic flow forecasting[J]. IEEE Internet of Things Journal, 2023, 10(13): 11518–11532. doi: 10.1109/JIOT.2023.3243122.
[5]	LI Biyue, LI Zhishuai, CHEN Jun, et al. MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction[J]. Transportation Research Part C: Emerging Technologies, 2024, 160: 104521. doi: 10.1016/j.trc.2024.104521.
[6]	LI Zhuolin, ZHANG Gaowei, YU Jie, et al. Dynamic graph structure learning for multivariate time series forecasting[J]. Pattern Recognition, 2023, 138: 109423. doi: 10.1016/j.patcog.2023.109423.
[7]	WU Di, PENG Kai, WANG Shangguang, et al. Spatial–temporal graph attention gated recurrent transformer network for traffic flow forecasting[J]. IEEE Internet of Things Journal, 2024, 11(8): 14267–14281. doi: 10.1109/JIOT.2023.3340182.
[8]	LIU Shipeng and WANG Xingjian. An improved transformer based traffic flow prediction model[J]. Scientific Reports, 2025, 15(1): 8284. doi: 10.1038/s41598-025-92425-7.
[9]	SHAO Zhiqi, WANG Ze, YAO Xusheng, et al. ST-MambaSync: Complement the power of mamba and transformer fusion for less computational cost in spatial-temporal traffic forecasting[J]. Information Fusion, 2025, 117: 102872. doi: 10.1016/j.inffus.2024.102872.
[10]	LI Yaguang, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting[C]. The 6th International Conference on Learning Representations, Vancouver, Canada, 2018: 1–16.
[11]	GUO Shengnan, LIN Youfang, FENG Ning, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]. The 33rd AAAI Conference on Artificial Intelligence, Honolulu, USA, 2019: 922–929. doi: 10.1609/aaai.v33i01.3301922.
[12]	YE Yaqin, XIAO Yue, ZHOU Yuxuan, et al. Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents[J]. Expert Systems with Applications, 2023, 234: 121101. doi: 10.1016/j.eswa.2023.121101.
[13]	ZHENG Chuanpan, FAN Xiaoliang, WANG Cheng, et al. GMAN: A graph multi-attention network for traffic prediction[C]. The 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 1234–1241. doi: 10.1609/aaai.v34i01.5477.
[14]	DENG Jinliang, CHEN Xiusi, JIANG Renhe, et al. ST-norm: Spatial and temporal normalization for multi-variate time series forecasting[C]. The 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, 2021: 269–278. doi: 10.1145/3447548.3467330.
[15]	SHAO Zhiqi, BELL M G H, WANG Ze, et al. St-mamba: Spatial-temporal selective state space model for traffic flow prediction[EB/OL]. https://arxiv.org/abs/2404.13257, 2024.
[16]	CHOI J, KIM H, AN M, et al. SpoT-mamba: Learning long-range dependency on spatio-temporal graphs with selective state spaces[C]. The 3rd International Workshop on Spatio-Temporal Reasoning and Learning (STRL 2024) co-located with the 33rd International Joint Conference on Artificial Intelligence, Jeju island, Korea, 2024.