@Article{cmc.2025.072147,
AUTHOR = {Guangyu Huo, Chang Su, Xiaoyu Zhang, Xiaohui Cui, Lizhong Zhang},
TITLE = {State Space Guided Spatio-Temporal Network for Efficient Long-Term Traffic Prediction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {2},
PAGES = {1--23},
URL = {http://www.techscience.com/cmc/v86n2/64795},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2025.072147}
}