Integrated spatial-temporal feature alignment with graph convolutional and gated recurrent networks for traffic flow prediction.
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| Title: | Integrated spatial-temporal feature alignment with graph convolutional and gated recurrent networks for traffic flow prediction. |
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| Authors: | Ata KI; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia.; Department of Computer Engineering, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia., Hassan MK; Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malysia, UPM Serdang, Selangor Darul Ehsan, Malaysia., Al-Haddad SAR; Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malysia, UPM Serdang, Selangor Darul Ehsan, Malaysia., Alquthami T; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia., Rahman RZA; Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malysia, UPM Serdang, Selangor Darul Ehsan, Malaysia., Alani S; Electronic Computer Center, University of Anbar, Iraq., Hoque MA; Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malysia, UPM Serdang, Selangor Darul Ehsan, Malaysia. |
| Source: | PloS one [PLoS One] 2026 Apr 28; Vol. 21 (4), pp. e0337661. Date of Electronic Publication: 2026 Apr 28 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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