Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting

Author:

Xu ChenyangORCID,Xu Changqing

Publisher

Elsevier BV

Reference50 articles.

1. Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks;Ali;Information Sciences,2021

2. Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction;Ali;Neural Networks,2022

3. IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction;An;Neural Networks,2021

4. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of the 3rd international conference on learning representations.

5. A3T-GCN: Attention temporal graph convolutional network for traffic forecasting;Bai;ISPRS International Journal of Geo-Information,2021

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