TY - GEN
T1 - Empowering Spatiotemporal Traffic Prediction through Fusing Flowformer and GNN
AU - Hou, Yuntian
AU - Zhang, Di
AU - Niu, Qiang
N1 - Publisher Copyright:
©2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This study introduces STFLformer, a hybrid model that integrates GNNs with Flowformer for urban traffic prediction. By leveraging GNNs to model spatial dependencies and Flowformer for capturing temporal patterns, STFLformer effectively addresses complex spatio-temporal dynamics in traffic systems. Extensive experiments conducted on six benchmark datasets, including METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08, demonstrate the model’s superior performance across diverse traffic scenarios. Notably, STFLformer consistently outperforms state-of-the-art models, highlighting its robustness and adaptability to real-world traffic patterns. These findings underscore the potential of advanced neural architectures in fostering intelligent traffic management, promoting sustainable urban mobility, and contributing to improved air quality.
AB - This study introduces STFLformer, a hybrid model that integrates GNNs with Flowformer for urban traffic prediction. By leveraging GNNs to model spatial dependencies and Flowformer for capturing temporal patterns, STFLformer effectively addresses complex spatio-temporal dynamics in traffic systems. Extensive experiments conducted on six benchmark datasets, including METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08, demonstrate the model’s superior performance across diverse traffic scenarios. Notably, STFLformer consistently outperforms state-of-the-art models, highlighting its robustness and adaptability to real-world traffic patterns. These findings underscore the potential of advanced neural architectures in fostering intelligent traffic management, promoting sustainable urban mobility, and contributing to improved air quality.
KW - Graph Neural Networks
KW - Spatio-temporal Embeddings
KW - Traffic Prediction
KW - Transformer Models
UR - https://www.scopus.com/pages/publications/105010186347
U2 - 10.1109/AINIT65432.2025.11035686
DO - 10.1109/AINIT65432.2025.11035686
M3 - Conference Proceeding
AN - SCOPUS:105010186347
T3 - 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2025
SP - 1206
EP - 1211
BT - 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2025
Y2 - 11 April 2025 through 13 April 2025
ER -
