PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction.

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作者： Jin,Fan ^[1] ; Wenchao,Weng ^[2] ; Qikai,Chen ^[3] ; Huifeng,Wu ^[4] ; Jia,Wu ^[5]

作者单位： Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, Hangzhou, China. Electronic address: fanjin@hdu.edu.cn. ^[1] Zhejiang University of Technology, Hangzhou, China. Electronic address: 111124120010@zjut.edu.cn. ^[2] Hangzhou Dianzi University, Hangzhou, China. Electronic address: 20321310@hdu.edu.cn. ^[3] Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, Hangzhou, China. Electronic address: whf@hdu.edu.cn. ^[4] Macquarie University, Sydney, Australia. Electronic address: jia.wu@mq.edu.au. ^[5]

关键词 Graph convolutional network Graph structure learning Periodic features Spatio-temporal model Traffic flow prediction

主题词人类(Humans);算法(Algorithms)

DOI 10.1016/j.neunet.2024.106941

PMID 39642644

发布时间 2025-01-20

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Neural networks

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PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction.

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PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction.

Neural networks

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