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Adaptive Graph Convolution Networks for Traffic Flow Forecasting

Traffic flow forecasting is a highly challenging task due to the dynamic spatial-temporal road conditions. Graph neural networks (GNN) has been widely applied in this task. However, most of these GNNs ignore the effects of time-varying road conditions due to the fixed range of the convolution receptive field.

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Cite this as

Zhengdao Li, Wei Li, Kai Hwang (2024). Dataset: Adaptive Graph Convolution Networks for Traffic Flow Forecasting. https://doi.org/10.57702/3ncedg73

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Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Defined In	https://doi.org/10.48550/arXiv.2307.05517
Author	Zhengdao Li
More Authors	Wei Li Kai Hwang
Homepage	https://github.com/zhengdaoli/AGC-net