Pongsakon Promsawat1, Weerapan Sae-dan2,*, Marisa Kaewsuwan3, Weerawat Sudsutad3, Aphirak Aphithana3
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 579-607, 2025, DOI:10.32604/cmes.2024.057774
- 17 December 2024
Abstract The ability to accurately predict urban traffic flows is crucial for optimising city operations. Consequently, various methods for forecasting urban traffic have been developed, focusing on analysing historical data to understand complex mobility patterns. Deep learning techniques, such as graph neural networks (GNNs), are popular for their ability to capture spatio-temporal dependencies. However, these models often become overly complex due to the large number of hyper-parameters involved. In this study, we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks (DMST-GNODE), a framework based on ordinary differential equations (ODEs) that autonomously discovers effective spatial-temporal… More >
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