Graph Neural Networks Empowered Origin‐Destination Learning for Urban Traffic Prediction Article Swipe
Chuanting Zhang
,
Guoqing Ma
,
Liang Zhang
,
Basem Shihada
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/cit2.70021
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/cit2.70021
Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality. The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics. Existing approaches mainly focus on modelling the traffic data itself, but do not explore the traffic correlations implicit in origin‐destination (OD) data. In this paper, we propose STOD‐Net, a dynamic spatial‐temporal OD feature‐enhanced deep network, to simultaneously predict the in‐traffic and out‐traffic for each and every region of a city. We model the OD data as dynamic graphs and adopt graph neural networks in STOD‐Net to learn a low‐dimensional representation for each region. As per the region feature, we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations. To further capture the complicated spatial and temporal dependencies among different regions, we propose a novel joint feature, learning block in STOD‐Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal‐aware. We evaluate the effectiveness of STOD‐Net on two benchmark datasets, and experimental results demonstrate that it outperforms the state‐of‐the‐art by approximately 5% in terms of prediction accuracy and considerably improves prediction stability up to 80% in terms of standard deviation.
Related Topics
Concepts
Computer science
Block (permutation group theory)
Feature (linguistics)
Feature learning
Benchmark (surveying)
Graph
Artificial intelligence
Data mining
Deep learning
Artificial neural network
Machine learning
Theoretical computer science
Geography
Mathematics
Linguistics
Geodesy
Geometry
Philosophy
Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1049/cit2.70021
- OA Status
- gold
- Cited By
- 1
- References
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- Related Works
- 10
- OpenAlex ID
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https://doi.org/10.1049/cit2.70021Digital Object Identifier
- Title
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Graph Neural Networks Empowered Origin‐Destination Learning for Urban Traffic PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-05-29Full publication date if available
- Authors
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Chuanting Zhang, Guoqing Ma, Liang Zhang, Basem ShihadaList of authors in order
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https://doi.org/10.1049/cit2.70021Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1049/cit2.70021Direct OA link when available
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Computer science, Block (permutation group theory), Feature (linguistics), Feature learning, Benchmark (surveying), Graph, Artificial intelligence, Data mining, Deep learning, Artificial neural network, Machine learning, Theoretical computer science, Geography, Mathematics, Linguistics, Geodesy, Geometry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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