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<p>The increasing emphasis on traffic flow prediction in urban traffic management and planning is being driven by the continuous development of urbanization. This study aims to investigate the necessity of introducing graph structure information in data contexts with relatively simple road structures. We conduct experimental investigations to analyze the role of introducing graph structure information by comparing a graph neural network model with graph structure information and our proposed Spatial-Temporal Convolutional Long Short-Term Memory Network (ST-ConvLSTMNet) model, which is designed to capture complex spatiotemporal dependencies efficiently,​​ using six types of actual traffic data. Moreover, we analyze the application of various graph neural network models in traffic flow prediction, comparing their accuracies and computational efficiencies. Lastly, we provide a detailed explanation of the characteristics of data with relatively simple road structures and conduct further exploration of each module’s functionality through ablation studies. Codes are available at <a href="https://github.com/CC10969Peng">https://github.com/CC10969Peng</a>.</p>

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Simplicity meets power: robust traffic flow prediction with ST-ConvLSTMNet model

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Cheng Peng,
  • Yuan Cheng,
  • Ao Li

摘要

The increasing emphasis on traffic flow prediction in urban traffic management and planning is being driven by the continuous development of urbanization. This study aims to investigate the necessity of introducing graph structure information in data contexts with relatively simple road structures. We conduct experimental investigations to analyze the role of introducing graph structure information by comparing a graph neural network model with graph structure information and our proposed Spatial-Temporal Convolutional Long Short-Term Memory Network (ST-ConvLSTMNet) model, which is designed to capture complex spatiotemporal dependencies efficiently,​​ using six types of actual traffic data. Moreover, we analyze the application of various graph neural network models in traffic flow prediction, comparing their accuracies and computational efficiencies. Lastly, we provide a detailed explanation of the characteristics of data with relatively simple road structures and conduct further exploration of each module’s functionality through ablation studies. Codes are available at https://github.com/CC10969Peng.