MFSTGN: a multi-scale spatial-temporal fusion graph network for traffic prediction

Author:

Tian Ran,Wang ChuORCID,Hu Jia,Ma Zhongyu

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference45 articles.

1. Li F, Feng J, Yan H et al (2023) Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Trans Knowl Discov Data 17(1):1–21

2. Wang X, Ma Y, Wang Y et al (2020) Traffic flow prediction via spatial temporal graph neural network. Proc Web Conf 2020:1082–1092

3. Zheng C, Fan X, Wang C et al (2020) GMAN: A graph multi-attention network for traffic prediction. Proc AAAI Conf Artif Intell 34(01):1234–1241

4. Li Y, Yu R, Shahabi C, et al. (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. International Conference on Learning Representations

5. Cascetta E. (2013) Transportation systems engineering: theory and methods. Springer Science Business Media

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