Conference Paper2021

SST-GNN: simplified spatio-temporal traffic forecasting model using graph neural network

Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali, M Ashraful Amin, AKM Mahbubur Rahman

Pacific-Asia Conference on Knowledge Discovery and Data Mining

Springer International Publishing, pp. 90–102, ISBN: 9783030757670

DOI: 10.1007/978-3-030-75768-7_8URL

CCDS Authors

M Ashraful Amin, PhD

M Ashraful Amin, PhD

Founder DirectorDirector, Artificial Intelligence & Machine Learing WingDirector, Human-Computer Interaction WingDirector, Data Science Wing

References

  1. 1.Chen, Tianyu, Wang, Zeyu, Li, Lin, Li, Ding, Li, Zongyang, Chang, Xiaoning, Bian, Pan, Liang, Guangtai, Wang, Qianxiang, Xie, Tao. (2025). Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories. Dagstuhl Research Online Publication Server[10.4230/lipics.ecoop.2025.6]
  2. 2.Michaël Defferrard. (2016). Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Gdl Seminar). Figshare[10.5281/zenodo.1318397]
  3. 3.Bing Yu, Haoteng Yin, Zhanxing Zhu. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. , 3634–3640[10.24963/ijcai.2018/505]
  4. 4.Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan. (2019). Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 922–929[10.1609/aaai.v33i01.3301922]
  5. 5.Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang. (2019). Graph WaveNet for Deep Spatial-Temporal Graph Modeling. , 1907–1913[10.24963/ijcai.2019/264]
  6. 6.Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, Xu Sun. (2020). Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3438–3445[10.1609/aaai.v34i04.5747]
  7. 7.Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong. (2020). LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. , 2355–2361[10.24963/ijcai.2020/326]
  8. 8.Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng. (2019). Gated Residual Recurrent Graph Neural Networks for Traffic Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 485–492[10.1609/aaai.v33i01.3301485]
  9. 9.Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. (2019). GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. , 2286–2293[10.24963/ijcai.2019/317]
  10. 10.Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Ki-Hwan Kim, Sungahn Ko, Jaegul Choo. (2020). ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed. , 1215–1224[10.1145/3340531.3411940]
  11. 11.William L. Hamilton, Rex Ying, Jure Leskovec. (2017). Inductive Representation Learning on Large Graphs. arXiv (Cornell University), 30, 1024–1034[10.48550/arxiv.1706.02216]
  12. 12.Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. (2017). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic\n Forecasting. arXiv (Cornell University)[10.48550/arxiv.1707.01926]
  13. 13.Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra. (2020). Beyond Homophily in Graph Neural Networks: Current Limitations and\n Effective Designs. arXiv (Cornell University)[10.48550/arxiv.2006.11468]
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