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.

翻译：本研究深入探讨了图神经网络在交通预测领域的应用，该领域是智能交通系统的关键组成部分。准确的交通预测对于智能交通系统中的行程规划、交通控制及车辆路径规划等功能至关重要。本文在交通预测背景下，重点研究了三种主流GNN架构：图卷积网络、图采样与聚合网络，以及门控图神经网络。我们详细剖析了每种架构的方法论，包括层配置、激活函数及超参数设置。研究核心目标是最小化预测误差，其中门控图神经网络被证实为三种模型中最优选择。本文系统阐述了各架构的预测结果，通过均方根误差和平均绝对误差指标量化其预测性能。假设性结果揭示了重要发现：GCN的RMSE为9.10、MAE为8.00；GraphSAGE性能有所提升，RMSE为8.3、MAE为7.5；而门控图神经网络以最低的RMSE值9.15和出色的MAE值7.1脱颖而出，被定位为最优模型。