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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]
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Database: Engineering Source
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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]
ISSN:09424962
DOI:10.1007/s00530-025-02195-5