KaTaGCN: Knowledge-Augmented and Time-Aware Graph Convolutional Network for efficient traffic forecasting
Author:
Funder
National Natural Science Foundation of China
Sichuan Province Science and Technology Support Program
Publisher
Elsevier BV
Reference51 articles.
1. A survey of traffic prediction: from spatio-temporal data to intelligent transportation;Yuan;Data Sci. Eng.,2021
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3. Generic dynamic graph convolutional network for traffic flow forecasting;Xu;Inf. Fusion,2023
4. Temporal metrics based aggregated graph convolution network for traffic forecasting;Chen;Neurocomputing,2023
5. Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues;Bui;Appl. Intell.,2021
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