Over the past few years, the graph-based Graph Convolutional Network (GCN) models have been introduced to handle the spatial-temporal attributes in short-term traffic prediction. Aiming at filling the gap of lacking comparisons of different graph-based models, this study analyses the performances of different graph-based convolutional neural networks. A modified sequence to sequence structure with attention mechanism and the residual module is applied as the backbone of the prediction model, and the input to the graph-based models is the Maximal Information Coefficient (MIC) adjacency matrix. The results confirm the effectiveness of the proposed prediction framework and shed light on choosing the most acceptable graph-based convolutional neural networks for short-term traffic predictions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analysis of Different Graph Convolutional Network Prediction Models with Spatial Dependence Evaluation


    Contributors:
    Chen, Yuting (author) / Mao, Jiannan (author) / Huang, Hao (author) / Yan, Qipeng (author) / Lu, Weike (author) / Liu, Lan (author)


    Publication date :

    2021-09-19


    Size :

    1007612 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction

    Xu, Xuran / Zhang, Tong / Xu, Chunyan et al. | IEEE | 2023



    Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network

    Wang, Hanqiu / Zhang, Rongqing / Cheng, Xiang et al. | IEEE | 2022


    Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks

    Heglund, Jacob S.W. / Taleongpong, Panukorn / Hu, Simon et al. | IEEE | 2020