STGNN-FAM: A Traffic Flow Prediction Model for Spatiotemporal Graph Networks Based on Fusion of Attention Mechanisms

• Published in: Journal of advanced transportation 2023-05, Vol.2023, p.1-19
• Main Authors: Qi, Xueying, Hu, Weijian, Li, Baoshan, Han, Ke
• Format: Article
• Language: English
• Subjects: Accuracy; Benchmarks; Communications traffic; Data transmission; Deep learning; Feature extraction; Kalman filters; Machine learning; Methods; Neural networks; Prediction models; Queuing theory; Roads & highways; Sensors; Time series; Traffic flow; Traffic information; Traffic models; Traffic speed; Transportation
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2023/8880530

Network traffic state prediction has been constantly challenged by complex spatiotemporal features of traffic information as well as imperfection in streaming data. This paper proposes a traffic flow prediction model for spatiotemporal graph networks based on fusion of attention mechanisms (STGNN-FAM) to simultaneously tackle these challenges. This model contains a spatial feature extraction layer, a bidirectional temporal feature extraction layer, and an attention fusion layer, which not only fully considers the temporal and spatial features of the traffic flow problem but also uses the attention mechanism to enhance the critical temporal and spatial features to achieve more accurate and robust predictions. Experimental results on a network traffic speed dataset PeMSD7 show that the proposed STGNN-FAM outperforms several important benchmarks in prediction accuracy and the ability to withstand interference in the data stream, especially for mid- and long-term prediction of 30 minutes and 45 minutes.