Network Wide Traffic

Network-wide traffic prediction aims to forecast traffic conditions across an entire transportation network, enabling improved traffic management and user experience. Current research heavily utilizes graph neural networks (GNNs), often incorporating spatiotemporal features and exogenous data like weather or lane closures, to enhance prediction accuracy and robustness. These models are evaluated using metrics such as MAE, RMSE, and MAPE, with a focus on improving efficiency and transferability across different cities and network topologies. The resulting advancements have significant implications for intelligent transportation systems, enabling more effective traffic control strategies and real-time information dissemination.