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Dynamic traffic bottlenecks identification based on congestion diffusion model by influence maximization in metro-city scales Journal article
Concurrency Computation, 2021,Volume: 33,Issue: 6
Authors:  Zhao,Baoxin;  Xu,Cheng Zhong;  Liu,Siyuan;  Zhao,Juanjuan;  Li,Li
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bottlenecks identification  influence maximization  traffic congestion diffusion  traffic flow influence  
Spatiotemporal data fusion in graph convolutional networks for traffic prediction Journal article
IEEE Access, 2020,Volume: 8,Page: 76632-76641
Authors:  Zhao, Baoxin;  Gao, Xitong;  Liu, Jianqi;  Zhao, Juanjuan;  Xu, Chengzhong
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Data Fusion  Graph Convolutional Networks  Multi-source Data  Traffic Prediction  
COMO: Widening Deep Neural Networks with COnvolutional MaxOut Journal article
IEEE Transactions on Multimedia, 2020
Authors:  Zhao,Baoxin;  Xiong,Haoyi;  Bian,Jiang;  Guo,Zhishan;  Xu,Cheng Zhong;  Dou,Dejing
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Computer architecture  Convolution  Convolutional neural networks  Deep learning  Spatial resolution  Transforms  
A Congestion Diffusion Model with Influence Maximization for Traffic Bottlenecks Identification in Metrocity Scales Conference paper
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Authors:  Zhao,Baoxin;  Xu,Chengzhong;  Liu,Siyuan;  Zhao,Juanjuan;  Li,Li
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2021/03/09
Bottlenecks identification  influence maximization  traffic congestion diffusion  traffic flow influence