Spatial–Temporal Traffic Prediction Model Based on Adaptive Graphs Fusion and Dual-Graph Collaborative Convolution-Reference-Cited by-同舟云学术

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Spatial–Temporal Traffic Prediction Model Based on Adaptive Graphs Fusion and Dual-Graph Collaborative Convolution

Published:2024-06-30 Issue: Volume:21 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Cheng Zhen¹,Qiu Song¹,Sun Li²,Han Dingding³,Li Qingli²,Chen Mingsong¹

Affiliation:

1. East China Normal University,MOE Eng. Research Center of SW/HW Co-Design Tech. And App.,Shanghai,China,200062

2. East China Normal University,Shanghai Key Lab. of Multidimensional Information Processing,Shanghai,China,200241

3. Fudan University,School of Information Science and Technology,Shanghai,China,200433

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650008.pdf?arnumber=10650008

Reference30 articles.

1. Orthogonal Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

2. MonitorLight

3. Convolutional neural networks on graphs with fast localized spectral filtering;Defferrard

4. Using LSTM and GRU neural network methods for traffic flow prediction

5. Predicting traffic congestion using recurrent neural networks;Zhou

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