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Forecasting traffic flows in irregular regions with multi-graph 被引量:2
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作者 Dewen SENG Fanshun LV +2 位作者 Ziyi LIANG Xiaoying SHI qiming fang 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2021年第9期1179-1193,共15页
The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural netwo... The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods. 展开更多
关键词 Traffic flow prediction Multi-graph convolutional network Gated recurrent unit Irregular regions
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Modern deep learning in bioinformatics 被引量:1
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作者 Haoyang Li Shuye Tian +6 位作者 Yu Li qiming fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao 《Journal of Molecular Cell Biology》 SCIE CAS CSCD 2020年第11期823-827,共5页
Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A... Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials. 展开更多
关键词 HAS SUCH SHE
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