Transformer network with decoupled spatial–temporal embedding for traffic flow forecasting
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05126-x.pdf
Reference45 articles.
1. Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process Syst 33:17804–17815
2. Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp 753–763
3. Zheng C, Fan X, Wang C, Qi J (2020) Gman: A graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence. 34(01):1234–1241
4. Feng A, Tassiulas L (2022) Adaptive graph spatial-temporal transformer network for traffic forecasting. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp 3933–3937
5. Yan H, Ma X, Pu Z (2021) Learning dynamic and hierarchical traffic spatiotemporal features with transformer. IEEE Trans Intell Transp Syst 23(11):22386–22399
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