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This study delves into the application of graph neural networks in the realmof 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 GNNarchitectures Graph Convolutional Networks (Graph Sample and Aggregation) andGated Graph Neural Networks are explored within the context of trafficprediction. Each architecture's methodology is thoroughly examined, includinglayer configurations, activation functions,and hyperparameters. The primarygoal is to minimize prediction errors, with GGNNs emerging as the mosteffective choice among the three models. The research outlines outcomes foreach architecture, elucidating their predictive performance through root meansquared error and mean absolute error (MAE). Hypothetical results revealintriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, whileGraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated GraphNeural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAEof 7.1, positioning them as the frontrunner.

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