Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction

2018 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm.2018.00107 ◽

2018 ◽

Author(s):

Cen Chen ◽

Kenli Li ◽

Sin G. Teo ◽

Guizi Chen ◽

Xiaofeng Zou ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Temporal Correlations ◽

Flow Prediction ◽

Spatio Temporal

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Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3385414 ◽

2020 ◽

Vol 14 (4) ◽

pp. 1-23 ◽

Author(s):

Cen Chen ◽

Kenli Li ◽

Sin G. Teo ◽

Xiaofeng Zou ◽

Keqin Li ◽

...

Keyword(s):

Neural Networks ◽

Traffic Flow ◽

Convolutional Neural Networks ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Spatio Temporal

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Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks

Information Sciences ◽

10.1016/j.ins.2021.08.042 ◽

2021 ◽

Author(s):

Ahmad Ali ◽

Yanmin Zhu ◽

Muhammad Zakarya

Keyword(s):

Neural Networks ◽

Traffic Flow ◽

Traffic Flow Prediction ◽

Temporal Correlations ◽

Flow Prediction ◽

Spatio Temporal

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Improved Spatio-Temporal Convolutional Neural Networks for Traffic Police Gestures Recognition

2020 16th International Conference on Computational Intelligence and Security (CIS) ◽

10.1109/cis52066.2020.00032 ◽

2020 ◽

Author(s):

Zhixuan Wu ◽

Nan Ma ◽

Yiu-ming Cheung ◽

Jiahong Li ◽

Qin He ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Traffic Police ◽

Spatio Temporal

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Deep Convolutional Neural Networks with Random Subspace Learning for Short-term Traffic Flow Prediction with Incomplete Data

2018 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2018.8489536 ◽

2018 ◽

Author(s):

Shijie Liao ◽

Jing Chen ◽

Jiaxin Hou ◽

Qingyu Xiong ◽

Junhao Wen

Keyword(s):

Neural Networks ◽

Traffic Flow ◽

Convolutional Neural Networks ◽

Incomplete Data ◽

Subspace Learning ◽

Random Subspace ◽

Deep Convolutional Neural Networks ◽

Traffic Flow Prediction ◽

Flow Prediction

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A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns

10.31223/osf.io/s2pjc ◽

2018 ◽

Author(s):

Ashesh Chattopadhyay ◽

Pedram Hassanzadeh ◽

Saba Pasha

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Test Case ◽

Climate Data ◽

Weather Patterns ◽

Spatio Temporal

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Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data

Scientific Reports ◽

10.1038/s41598-020-57897-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Ashesh Chattopadhyay ◽

Pedram Hassanzadeh ◽

Saba Pasha

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Test Case ◽

Climate Data ◽

Weather Patterns ◽

Spatio Temporal

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Blind quality assessment of omnidirectional videos using spatio-temporal convolutional neural networks

Optik ◽

10.1016/j.ijleo.2020.165887 ◽

2021 ◽

Vol 226 ◽

pp. 165887

Author(s):

Xiongli Chai ◽

Feng Shao

Keyword(s):

Neural Networks ◽

Quality Assessment ◽

Convolutional Neural Networks ◽

Spatio Temporal

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Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data

Applied Soft Computing ◽

10.1016/j.asoc.2018.09.040 ◽

2019 ◽

Vol 78 ◽

pp. 712-721 ◽

Author(s):

Shaojiang Deng ◽

Shuyuan Jia ◽

Jing Chen

Keyword(s):

Neural Networks ◽

Traffic Flow ◽

Convolutional Neural Networks ◽

Incomplete Data ◽

Temporal Relations ◽

Deep Convolutional Neural Networks ◽

Traffic Flow Prediction ◽

Flow Prediction

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Fuzzy hybrid framework with dynamic weights for short-term traffic flow prediction by mining spatio-temporal correlations

IET Intelligent Transport Systems ◽

10.1049/iet-its.2019.0287 ◽

2020 ◽

Vol 14 (2) ◽

pp. 73-81 ◽

Author(s):

Dongfang Ma ◽

Bowen Sheng ◽

Xiaolong Ma ◽

Sheng Jin

Keyword(s):

Traffic Flow ◽

Traffic Flow Prediction ◽

Temporal Correlations ◽

Flow Prediction ◽

Hybrid Framework ◽

Spatio Temporal

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Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

Renewable Energy ◽

10.1016/j.renene.2019.07.033 ◽

2020 ◽

Vol 146 ◽

pp. 760-768 ◽

Author(s):

Ziqian Kong ◽

Baoping Tang ◽

Lei Deng ◽

Wenyi Liu ◽

Yan Han

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Condition Monitoring ◽

Wind Turbines ◽

Spatio Temporal ◽

Gated Recurrent Units

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