  This study delves into the application of graph neural networks 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 (Graph Sample and Aggregation) and
Gated Graph Neural Networks 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 predictive performance through root mean
squared error and mean absolute error (MAE). 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.
