PGSFormer: traffic flow prediction based on joint optimization of progressive graph convolutional networks with subseries transformer

Key Points

• Traffic flow prediction improves with joint optimization of graph convolutional networks and transformers, enhancing accuracy.
• A notable increase in prediction performance is observed compared to traditional methods, with a measured improvement of 15%.
• Analysis utilizes progressive graph convolutional networks combined with a subseries transformer for effective modeling.
• These findings indicate significant potential for traffic management strategies, highlighting the need for further model validation.