TY  - EJOU
AU  - Huo, Guangyu 
AU  - Su, Chang 
AU  - Zhang, Xiaoyu 
AU  - Cui, Xiaohui 
AU  - Zhang, Lizhong 

TI  - State Space Guided Spatio-Temporal Network for Efficient Long-Term Traffic Prediction
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 2
SN  - 1546-2226

AB  - Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space model that efficiently captures long-term dependencies with near-linear computational complexity. The model combines Chebyshev polynomial-based graph convolutional networks (GCN) to explore spatial correlations. Additionally, we incorporate a multi-temporal feature capture mechanism, where the final integrated features are generated through the Hadamard product based on learnable parameters. This mechanism explicitly models short-term, daily, and weekly traffic patterns to enhance the network’s awareness of traffic periodicity. Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks, offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.
KW  - State space model; long-term traffic flow prediction; graph convolutional network; multi-time scale analysis; emerging applications at intelligent networks

DO  - 10.32604/cmc.2025.072147