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<p>Forecasting road network information based on spatio-temporal features has emerged as a key research direction in intelligent transportation. Despite numerous studies examining the intrinsic connection between complex spatial structures and temporal variations, achieving substantial improvements in forecasting remains challenging, particularly for long-term forecasting and handling abrupt changes. Extant research extensively leverages predefined graph structures or adaptive graphs to represent spatial relationships, but often overlooks the spatial dependence in node temporal information and the impact of road outliers. In response, this paper proposes a multiple spatio-temporal fusion network model (McFasten). Firstly, the spatio-temporal characteristics of the road network are analyzed using a multi-scale attention mechanism. Next, temporal information is decomposed, and anomalies are processed using Wavelet Convolution, and the spatial relationships inherent in the temporal sequences are captured by an improved Convolutional Block Attention Module (CBAM). Furthermore, the adjacency matrix is dynamically adjusted via a parameter matrix, enhancing the model’s capability for learning long-term implicit relationships. Finally, Efficient Multi-Scale Attention (EMA) is integrated into the output forecasting, achieving cross-dimensional feature integration. On real-world road network datasets, our proposed model outperforms the state-of-the-art baseline by up to 16%.</p>

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Optimized road traffic forecasting via multi-convolutional fusion and spatio-temporal feature extraction networks: An adaptive, anomaly-responsive framework

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Zhiwen Wang,
  • Jingxiao Zeng,
  • Long Li,
  • Kangkang Yang,
  • Yaoke Shi,
  • Guobi Ling,
  • Jingjing Sun

摘要

Forecasting road network information based on spatio-temporal features has emerged as a key research direction in intelligent transportation. Despite numerous studies examining the intrinsic connection between complex spatial structures and temporal variations, achieving substantial improvements in forecasting remains challenging, particularly for long-term forecasting and handling abrupt changes. Extant research extensively leverages predefined graph structures or adaptive graphs to represent spatial relationships, but often overlooks the spatial dependence in node temporal information and the impact of road outliers. In response, this paper proposes a multiple spatio-temporal fusion network model (McFasten). Firstly, the spatio-temporal characteristics of the road network are analyzed using a multi-scale attention mechanism. Next, temporal information is decomposed, and anomalies are processed using Wavelet Convolution, and the spatial relationships inherent in the temporal sequences are captured by an improved Convolutional Block Attention Module (CBAM). Furthermore, the adjacency matrix is dynamically adjusted via a parameter matrix, enhancing the model’s capability for learning long-term implicit relationships. Finally, Efficient Multi-Scale Attention (EMA) is integrated into the output forecasting, achieving cross-dimensional feature integration. On real-world road network datasets, our proposed model outperforms the state-of-the-art baseline by up to 16%.