Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks

This study delves into the application of Graph Neural Networks (GNNs) in the realm of traffic forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions are vital for functions like trip planning, traffic control, and vehicle routing in such systems. Three prominent GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)—are explored within the context of traffic prediction. Each architecture’s methodology is thoroughly examined, including layer configurations, activation functions, and hyperparameters. The primary goal is to minimize prediction errors, with GGNNs emerging as the most effective choice among the three models. The research outlines outcomes for each architecture, elucidating their ability to anticipate using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)). Hypothetical results reveal intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE of 7.1, positioning them as the frontrunner. However, the study acknowledges result variability, emphasizing the influence of factors like dataset characteristics, graph structure, feature engineering, and hyperparameter tuning.