TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting-Reference-Cited by-同舟云学术

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TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Data Mining (ICDM)
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Author:

Ali Muhammad Afif,Venkatesan Suriya,Liang Victor,Kruppa Hannes

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9678506/9678989/09679077.pdf?arnumber=9679077

Reference16 articles.

1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

2. Sequence to sequence learning with neural networks;sutskever;Neural Information Processing Systems,2014

3. Grab-Posisi

4. Flat Minima

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2. A multi-channel spatial-temporal transformer model for traffic flow forecasting;Information Sciences;2024-06

3. Spatial–Temporal Traffic Modeling With a Fusion Graph Reconstructed by Tensor Decomposition;IEEE Transactions on Intelligent Transportation Systems;2024-02

4. A Survey on Graph Representation Learning Methods;ACM Transactions on Intelligent Systems and Technology;2024-01-16

5. MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction;IEEE Transactions on Intelligent Transportation Systems;2023-04

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