Traffic flow prediction with multi-feature spatio-temporal coupling based on peak time embedding-Reference-Cited by-同舟云学术

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Traffic flow prediction with multi-feature spatio-temporal coupling based on peak time embedding

Published:2024-07-26 Issue:16 Volume:80 Page:23442-23470
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Wei Siwei,Hu Dingbo,Wei Feifei,Liu Donghua,Wang Chunzhi

Funder

This research is funded by the National Natural Science Foundation of China under Grant

the Hubei Provincial Natural Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06378-1.pdf

Reference55 articles.

1. Zhang J, Wang F-Y, Wang K, Lin W-H, Xin X, Chen C (2011) Data-driven intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 12(4):1624–1639

2. Alam M, Ferreira J, Fonseca J (2016) Introduction to intelligent transportation systems, In: Transportation Systems: Dependable Vehicular Communications for Improved Road Safety, pp 1–17

3. Chaturvedi M, Srivastava S (2016) Multi-modal design of an intelligent transportation system. IEEE Trans Intell Transp Syst 18(8):2017–2027

4. Wang C et al (2023) STN-GCN: spatial and temporal normalization graph convolutional neural networks for traffic flow forecasting. Electronics 12(14):3158

5. Zhiying P, Yixue Y, Zhao H (2024) Multi-level spatial-temporal fusion neural network for traffic flow prediction. Clust Comput. https://doi.org/10.1007/s10586-024-04296-8

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