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- W4308081106 abstract "With the development of intelligent transportation systems, the potential need for city-level traffic prediction is emerging. However, some large-scale traffic forecasting methods divide urban areas into grids, which cannot achieve node-level prediction. Meanwhile, mainstream graph convolution models have limitations on the scale of the input graph. For this problem, we propose cluster graph convolution networks with spectral graph wavelet kernels. The proposed method divides the large graph into sub graphs and then builds localized spatial dependency with spectral graph wavelet convolution. The model could achieve node-level traffic prediction on a large graph with an end-to-end framework. The experiments are conducted on the historical dataset of Beijing, covering more than 8,000 road segments. The experimental results demonstrate the effectiveness of the proposed method." @default.
- W4308081106 created "2022-11-08" @default.
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- W4308081106 date "2022-10-08" @default.
- W4308081106 modified "2023-09-28" @default.
- W4308081106 title "Towards City-Scale Traffic Prediction: Cluster Graph Wavelet Networks" @default.
- W4308081106 doi "https://doi.org/10.1109/itsc55140.2022.9921904" @default.
- W4308081106 hasPublicationYear "2022" @default.
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