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Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues

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Abstract

Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS). With the recent rapid advancement in deep learning, graph neural networks (GNNs) have become an emerging research issue for improving the traffic forecasting problem. Specifically, one of the main types of GNNs is the spatial-temporal GNN (ST-GNN), which has been applied to various time-series forecasting applications. This study aims to provide an overview of recent ST-GNN models for traffic forecasting. Particularly, we propose a new taxonomy of ST-GNN by dividing existing models into four approaches such as graph convolutional recurrent neural network, fully graph convolutional network, graph multi-attention network, and self-learning graph structure. Sequentially, we present experimental results based on the reconstruction of representative models using selected benchmark datasets to evaluate the main contributions of the key components in each type of ST-GNN. Finally, we discuss several open research issues for further investigations.

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Authors and Affiliations

  1. Viettel Cyberspace Center, Hanoi, Vietnam

    Khac-Hoai Nam Bui

  2. Korea Institute of Science and Technology Information, Seoul, South Korea

    Jiho Cho & Hongsuk Yi

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  1. Khac-Hoai Nam Bui
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  2. Jiho Cho
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  3. Hongsuk Yi
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Correspondence to Hongsuk Yi.

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This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korean Ministry of Science and ICT (MSIT) (No. 2018-0-00494, Development of deep learning-based urban traffic congestion prediction and signal control solution system) and Korea Institute of Science and Technology Information(KISTI) grant funded by the Korean Ministry of Science and ICT (MSIT) K-20-L02-C09-S01). Corresponding Author: Hongsuk Yi.

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Bui, KH.N., Cho, J. & Yi, H. Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues. Appl Intell 52, 2763–2774 (2022). https://doi.org/10.1007/s10489-021-02587-w

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