STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is sti...

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Bibliografiska uppgifter
Huvudupphovsmän: Zhihong Chang, Chunsheng Liu, Jianmin Jia
Materialtyp: Artikel
Språk:English
Publicerad: MDPI AG 2023-06-01
Serie:Applied Sciences
Ämnen:
traffic-flow prediction
dynamic characteristics
spatial–temporal self-attention graph convolutional network (STA-GCN)
Länkar:https://www.mdpi.com/2076-3417/13/11/6796

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https://www.mdpi.com/2076-3417/13/11/6796

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