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Open Access

ARTICLE

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok: An Application of a Continuous Convolutional Neural Network

Pongsakon Promsawat1, Weerapan Sae-dan2,*, Marisa Kaewsuwan3, Weerawat Sudsutad3, Aphirak Aphithana3
1 Department of Civil Engineering, Faculty of Engineering, Ramkhamkaeng University, Bangkok, 10240, Thailand
2 Department of Computer Engineering, Faculty of Engineering, Ramkhamkaeng University, Bangkok, 10240, Thailand
3 Department of Statistics, Faculty of Science, Ramkhamkaeng University, Bangkok, 10240, Thailand
* Corresponding Author: Weerapan Sae-dan. Email: email
(This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.057774

Received 27 August 2024; Accepted 16 October 2024; Published online 11 November 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 graph neural network (ST-GNN) architectures for traffic prediction tasks. The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets, consistently achieving the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, alongside the highest accuracy. On the BKK (Bangkok) dataset, it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval, maintaining this trend across 40 and 60 min. Similarly, on the PeMS08 dataset, DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min, demonstrating its effectiveness over longer periods. The Los_Loop dataset results further emphasise this model’s advantage, with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min, consistently maintaining superiority across all time intervals. These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Keywords

Graph neural networks; convolutional neural network; deep learning; dynamic multi-graph; spatio-temporal

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