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

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan marisa kaewsuwan Weerawat Sudsutad Aphirak Aphithana Department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** 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(STGNN)architectures for traffic prediction *** 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 *** 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 ***,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 *** 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 *** 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.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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Production of polyunsaturated fatty acids in the fern Pteris ensiformis suspension culture

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Asian Journal of Pharmaceutical Sciences 2016年第1期11卷 156-157页

作者： Sireewan kaewsuwan Adilah Salaeh Department of Pharmacognosy and Pharmaceutical Botany Faculty of Pharmaceutical SciencesPrince of Songkla UniversitySongkhla 90112Thailand Excellent Research Laboratory Phytomedicine and Pharmaceutical Biotechnology Excellence CenterFaculty of Pharmaceutical SciencesPrince of Songkla UniversitySongkhla 90112Thailand

Polyunsaturated fatty acids (PUFAs) are essential nutrients for a healthy diet and adequate human development (1)The human consumption of PUFAs is steadily increasing, but the production of fish oil, the main source of PUFAs for human consumption, is declining (2)Increasing PUFA demand has therefore raised an interest in achieving these PUFAs from alternative sources that are more economical and sustainable.

关键词： Pteris ensiformis Polyunsaturated fatty acids Suspension culture

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