Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors a...

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I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Seung Bae Jeon, Myeong-Hun Jeong
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	MDPI AG 2024-07-01
Rangatū:	Applied Sciences
Ngā marau:	traffic speed prediction spatio-temporal graph convolutional networks road network analysis hybrid model
Urunga tuihono:	https://www.mdpi.com/2076-3417/14/14/6102
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

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