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<p>In tackling the intricate challenges of temporal and spatial correlation, as well as the dynamic sequential nature inherent in traffic flow data. We present a pioneering solution called the 3D Grouped Convolution Recurrent Neural Network (3D-GCRNN). The 3D convolutions adeptly capture the network’s spatial intricacies across consecutive time steps, where channel grouping ensures an efficient reduction in the computational complexity of the convolutional operations. Furthermore, the Long-Short Term Memory (LSTM) recurrent units discern temporal features characterized by long-term dependencies. Extensive empirical experiments are conducted on the Highway Gantry datasets from Fuzhou. The results shows that our 3D-GCRNN exhibits predictive performance surpassing all baselines. Additionally, the visualization results vividly illustrate the model's adeptness in capturing the evolving dynamics of traffic states accurately. This work holds great promise in enhancing traffic management efficiency and ensuring the quality of residents' travel, offering essential technological support for transportation advancements.</p>

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A 3D grouped convolutional recurrent neural network for short-term traffic flow data prediction

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Zijun Cai,
  • Wangtu Xu,
  • Yanjie Wen

摘要

In tackling the intricate challenges of temporal and spatial correlation, as well as the dynamic sequential nature inherent in traffic flow data. We present a pioneering solution called the 3D Grouped Convolution Recurrent Neural Network (3D-GCRNN). The 3D convolutions adeptly capture the network’s spatial intricacies across consecutive time steps, where channel grouping ensures an efficient reduction in the computational complexity of the convolutional operations. Furthermore, the Long-Short Term Memory (LSTM) recurrent units discern temporal features characterized by long-term dependencies. Extensive empirical experiments are conducted on the Highway Gantry datasets from Fuzhou. The results shows that our 3D-GCRNN exhibits predictive performance surpassing all baselines. Additionally, the visualization results vividly illustrate the model's adeptness in capturing the evolving dynamics of traffic states accurately. This work holds great promise in enhancing traffic management efficiency and ensuring the quality of residents' travel, offering essential technological support for transportation advancements.