A period-extracted multi-featured dynamic graph convolution network for traffic demand prediction-Reference-Cited by-同舟云学术

A period-extracted multi-featured dynamic graph convolution network for traffic demand prediction

Published:2023-12-19 Issue:1 Volume:54 Page:722-737
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhu Yuntian,Ni Qingjian^ORCID

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05226-8.pdf

Reference43 articles.

1. Ke J, Qin X, Yang H, Zheng Z, Zhu Z, Ye J (2021) Predicting origin destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transportation Research Part C: Emerging Technologies 122:102858

2. Li Z, Richard YF, Yige W, Bin N, Tao T (2019) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398

3. Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv:1709.04875

4. Jiang R, Yin D, Wang Z, Wang Y, Deng J, Liu H, Cai Z, Deng J, Song X, Shibasaki R (2021) Dl-traff: survey and benchmark of deep learning models for urban traffic prediction. In: Proceedings of the 30th ACM international conference on information & knowledge management. CIKM’21, pp 4515-4525. Association for Computing Machinery, New York, USA

5. Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

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