An Attention based Spatial Temporal Graph Convolutional Networks for Traffic Flow Prediction - Venkatarama Reddy, Sreenath Marimakalapalli; Dammur, Annapurna; Narasimhamurthy, Anand (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The accurate and timely prediction of traffic flow is crucial for a safe and stable Intelligent Transportation System (ITS). Because of the complexity and nonlinearity of traffic flow, the conventional techniques fail to capture global and local correlations. To overcome this issue, an Attention-based Spatial Temporal-Graph Convolutional Network (AST-GCN) is proposed for predicting traffic flows. This research utilized PEMS04 and PEMS08 datasets which are publicly available transport network datasets. In the spatial dimension, the various locations' traffic conditions are influenced by each other, and mutual influence is extremely dynamic. In the temporal dimension, the exists a correlation among traffic conditions and the correlations differ under various situations. The GCN is used for extracting spatial and temporal features that are applied to graph-structured data directly and it builds a graph between two neural network layers that is a graph edge weight. The obtained result shows that the proposed AST-GCN model achieves a better MAPE of 8% on the PEMS04 dataset and 5.67% on PEMS08 dataset which ensures accurate prediction compared with other existing methods like Spatial-Temporal Correlation Graph Convolutional Networks (STCGCN), Long-term Spatial-Temporal Graph Convolutional Fusion Network (LSTFGCN) and Attention-based Spatial-Temporal Graph Transformer (ASTGT).