The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Open accessAbstract
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN.
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Traffic Flow Prediction via Spatial Temporal Graph Neural Network
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Human Action Recognition From Various Data Modalities: A Review
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Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
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Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327
Metadata
- Authors
- Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-09
- Volume
- 21
- Issue
- 9
- Pages
- 3848-3858
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2901504064
- Cited by
- 3,326
- References
- 43
- FWCI
- 136.55
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2020). T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2019.2935152
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