Smart City Gnosys

Smart city article details

Title	Transformer-Based Spatial-Temporal Graph Attention Network For Traffic Flow Prediction
ID_Doc	58803
Authors	Yan F.; Chen Q.
Year	2023
Published	Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023
DOI	http://dx.doi.org/10.1109/CyberC58899.2023.00031
Abstract	Traffic flow prediction, which plays an important role in intelligent traffic systems, has become a pressing problem to be addressed with the continuous development of smart cities. Currently, the fundamental obstacle lies in effectively modelling the complex spatial-temporal dependencies present in traffic flow data. Deep learning models such as Graph Neural Network based models and Transformer based models have shown promising results in this field. However, methods founded on a single model or framework have one significant limitation: Such methods cannot adequately represent the spatial and temporal features of traffic flow data, restricting the model's ability to learn the dynamics of urban transportation. In this paper, we propose a transformer-based spatial-temporal graph attention network model called TSTGAT for traffic flow prediction, which integrates Transformer and Graph Attention Network. Experiments on two real-world traffic datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed TSTGAT model outperforms well-known baselines. © 2023 IEEE.
Author Keywords	deep learning; graph neural network; Traffic flow prediction; transformer

Similar Articles

Id	Similarity	Authors	Title	Published
58593	0.934	Wang X.; Ma Y.; Wang Y.; Jin W.; Wang X.; Tang J.; Jia C.; Yu J.	Traffic Flow Prediction Via Spatial Temporal Graph Neural Network	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (2020)
11029	0.934	Zhang H.; Liu J.; Tang Y.; Xiong G.	Attention Based Graph Covolution Networks For Intelligent Traffic Flow Analysis	IEEE International Conference on Automation Science and Engineering, 2020-August (2020)
58579	0.927	Zhao J.	Traffic Flow Prediction Based On Adjacency Graph And Attention Mechanism	2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024 (2024)
35590	0.926	Remmouche B.; Boukraa D.; Zakharova A.; Bouwmans T.; Taffar M.	Long-Term Spatio-Temporal Graph Attention Network For Traffic Forecasting	Expert Systems with Applications, 288 (2025)
52517	0.925	Huang X.; Pan Z.; Zhao G.	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
52568	0.924	Ren H.; Kang J.; Zhang K.	Spatio-Temporal Graph-Tcn Neural Network For Traffic Flow Prediction	2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022 (2022)
11045	0.923	Yin S.; Wang J.; Cui Z.; Wang Y.	Attention-Enabled Network-Level Traffic Speed Prediction	2020 IEEE International Smart Cities Conference, ISC2 2020 (2020)
11043	0.922	He Q.; Xia D.; Li J.; Yang J.; Hu Y.; Li Y.; Li H.	Attention-Based Spatiotemporal Adaptive Graph Diffusion Convolutional Network For Traffic Flow Prediction	Transportation Research Record (2025)
58576	0.921	Zheng L.; Pu Y.; Sun W.	Traffic Flow Prediction Algorithm Based On Attention Spatiotemporal Graph Convolution Mechanism	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (2024)
44472	0.92	Nie X.; Peng J.; Wu Y.; Gupta B.B.; El-Latif A.A.A.	Real-Time Traffic Speed Estimation For Smart Cities With Spatial Temporal Data: A Gated Graph Attention Network Approach	Big Data Research, 28 (2022)