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Open Access

Peer-reviewed

Research Article

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction

Kang Xu,

Roles Writing – original draft

Affiliation School of Artificial Intelligence and Software, LiaoNing Petrochemical University, Fushun, China

https://orcid.org/0009-0001-4952-5022

⨯
Bin Pan ,

Roles Funding acquisition, Writing – review & editing

* E-mail: panbin@lnpu.edu.cn

Affiliation College of Science, LiaoNing Petrochemical University, Fushun, China
⨯
MingXin Zhang,

Roles Conceptualization

Affiliation College of Science, LiaoNing Petrochemical University, Fushun, China
⨯
Xuan Zhang,

Roles Data curation

Affiliation College of Information and Control Engineering, LiaoNing Petrochemical University, Fushun, China
⨯
XiaoYu Hou,

Roles Formal analysis

Affiliation College of Science, LiaoNing Petrochemical University, Fushun, China
⨯
JingXian Yu,

Roles Investigation

Affiliation College of Science, LiaoNing Petrochemical University, Fushun, China
⨯
ZhiZhu Lu,

Roles Validation

Affiliation Northeast Forestry University, Heilongjiang, China
⨯
Xiao Zeng,

Roles Visualization

Affiliation School of Artificial Intelligence and Software, LiaoNing Petrochemical University, Fushun, China
⨯
QingQing Jia

Roles Visualization

Affiliation College of Science, LiaoNing Petrochemical University, Fushun, China
⨯

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction

Kang Xu,
Bin Pan,
MingXin Zhang,
Xuan Zhang,
XiaoYu Hou,
JingXian Yu,
ZhiZhu Lu,
Xiao Zeng,
QingQing Jia

x

Published: June 11, 2025
https://doi.org/10.1371/journal.pone.0322145

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