Traffic forecasting is a branch of spatiotemporal forecasting that involves predicting future traffic speed or volume based on real-world data. It has a significant impact on urban mobility and quality of life, as it directly contributes to improving traffic management and trip planning. This study evaluates the performance of Graph Neural Networks (GNNs) in handling long-term forecasting, defined as predictions made up to 10 hours ahead. It addresses the evolution of performance and factors that may impact accuracy, such as fluctuations in traffic speed and road network configurations. The experiments are done using subsets of a benchmark dataset for traffic forecasting and a state-of-the-art GNN model. The findings showcase a logarithmic growth in prediction errors and the presence of two types of traffic jams—sudden and regular—along with their impact on prediction accuracy. Furthermore, the results highlight the complexity of quantifying the influence a factor has on forecasting performance, such as road network configuration or missing values.