Graph Neural Network for Traffic Flow Forecasting : Does an enriched adjacency matrix with low dimensional dataenhance the performance of GNN for traffic flow forecasting?

Abstract: Nowadays, machine learning methods are used in many applications and deployed in manyelectronic devices to solve problems and predict future states. One of the challenges mostbig cities confront is traffic jams since the roads are crammed with more and more vehicles, which will easily cause traffic congestion. Traffic jams are not environment-friendly,but scientific planning can minimize their effect. Traffic prediction is one of the most interesting subjects for Intelligent transportation systems due to its ability to prevent trafficjams with the knowledge of the predictions. Traffic prediction is a very challenging taskfor researchers to find or implement a model to perform accurately in different scenarios.Accurate traffic forecasting has become an essential mission for intelligent transportationsystems, which improve transportation efficiency, safety, and sustainability using moderntechnology and data analysis. Capturing both temporal and spatial dependencies is one ofthe most essential key in traffic prediction. Combining two or several models is one way tocapture both dependencies. A temporal graph convolutional network (T-GCN) is a graphneural network model, a combination of a graph convolutional network and a gated recurrent unit (GRU). In T-GCN, a graph convolutional network (GCN) is used to capture spatialwhile recurrent gated units to capture temporal dependencies. One of the main issues ofT-GCN is long-term prediction failure, where the model’s accuracy decreases when the prediction length increases. In this paper, we propose a Decomposed Temporal Self-AttentionMulti-layer Graph Convolutional network (DTSA-3GCN) to enhance overall traffic prediction in different horizons based on Singular Value Decomposition (SVD), Self-Attention(SA), and a Temporal Multi-layer Graph Convolutional Network. The experiment resultdemonstrates that DTSA-3GCN outperforms the state-of-the-art models such as T-GCN,A3T-GCN, and STGODE.