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- W3210135239 abstract "Due to the complex routes and the dynamic changing factors in transportation, precise traffic speed prediction is very difficult. Traditional prediction methods only focus on a single monitoring site, without establishing a relationship between different sites, so the precision is poor. The deep learning method can model traffic networks well, but suffers from information loss and the disadvantage of single input data. A multisource spatio-temporal hybrid dilated graph convolutional network (GCN) for forecasting traffic speed is proposed in this paper. A GCN based on hybrid dilated convolution can extract the influence of adjacent information and capture dynamic spatial and non-linear temporal correlations. Considering multisource data will increase the forecasting precision and improve the generalisation ability. Using a real-world data set, the performance of the proposed model was validated against other baselines (a fully connected neural network, convolutional neural network and spatio-temporal GCN). The proposed model was found to be superior to other models as it considers proximity information, which is often overlooked, and multifactorial influence." @default.
- W3210135239 created "2021-11-08" @default.
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- W3210135239 date "2021-10-26" @default.
- W3210135239 modified "2023-09-26" @default.
- W3210135239 title "Multi-source Spatio-temporal Hybrid Dilated Graph Convolutional Network for Traffic Speed Forecasting" @default.
- W3210135239 doi "https://doi.org/10.1680/jtran.21.00024" @default.
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