A spatio-temporal sequence-to-sequence network for traffic flow prediction

Author:

Cao Shuqin,Wu Libing,Wu Jia,Wu Dan,Li Qingan

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

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

2. Atwood, J., Towsley, D., 2016. Diffusion-convolutional neural networks, in: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, pp. 1993–2001.

3. Neural machine translation by jointly learning to align and translate;Bahdanau;International Conference on Learning Representations,2015

4. Adaptive graph convolutional recurrent network for traffic forecasting;Bai;Adv. Neural Inform. Process. Syst.,2020

5. Bai, S., Kolter, J.Z., Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

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