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Adaptive spatial-temporal graph ODE networks for traffic flow forecasting.

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Title: Adaptive spatial-temporal graph ODE networks for traffic flow forecasting.
Authors: Han, Shixiang1 (AUTHOR) 24110111@bjtu.edu.cn, Wang, Xu1 (AUTHOR) xu.wang@bjtu.edu.cn, Jin, Yi1 (AUTHOR) yjin@bjtu.edu.cn, Feng, Songhe1 (AUTHOR) shfeng@bjtu.edu.cn, Lang, Congyan1 (AUTHOR) cylang@bjtu.edu.cn, Li, Yidong1 (AUTHOR) ydli@bjtu.edu.cn
Source: Multimedia Systems. Jun2026, Vol. 32 Issue 4, p1-14. 14p.
Subjects: Graph neural networks, Continuous time models, Traffic estimation
Abstract: Traffic flow prediction is a fundamental task in spatial-temporal forecasting; however, it remains highly challenging due to the intricate interdependencies between spatial and temporal dynamics. Existing graph convolutional network (GNN)-based methods face two key limitations: (1) the assumption of static graph structures limits their ability to model dynamic spatial-temporal heterogeneity; and (2) the reliance on discrete time-slice processing hinders the capture of continuous traffic dynamics. To address these challenges, we propose Adaptive Spatial-temporal Graph ODE Networks (ASTGODE), which leverage Neural ODEs to reformulate discrete spatial-temporal convolutions into a continuous modeling paradigm. ASTGODE further incorporates multi-modal graph convolutions to capture heterogeneous spatial dependencies, and a dynamic spatial-temporal adaptation module to address feature interaction heterogeneity. Extensive experiments on real-world traffic datasets demonstrate consistent improvements over existing baselines, validating its enhanced capability in spatial-temporal representation learning. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Traffic flow prediction is a fundamental task in spatial-temporal forecasting; however, it remains highly challenging due to the intricate interdependencies between spatial and temporal dynamics. Existing graph convolutional network (GNN)-based methods face two key limitations: (1) the assumption of static graph structures limits their ability to model dynamic spatial-temporal heterogeneity; and (2) the reliance on discrete time-slice processing hinders the capture of continuous traffic dynamics. To address these challenges, we propose Adaptive Spatial-temporal Graph ODE Networks (ASTGODE), which leverage Neural ODEs to reformulate discrete spatial-temporal convolutions into a continuous modeling paradigm. ASTGODE further incorporates multi-modal graph convolutions to capture heterogeneous spatial dependencies, and a dynamic spatial-temporal adaptation module to address feature interaction heterogeneity. Extensive experiments on real-world traffic datasets demonstrate consistent improvements over existing baselines, validating its enhanced capability in spatial-temporal representation learning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00530-025-02195-5
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Continuous time models
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      – SubjectFull: Traffic estimation
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              Text: Jun2026
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              Y: 2026
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