This chapter discusses the challenges and future directions in graph neural networks (GNN) for traffic prediction. Key challenges include data heterogeneity, multimodality, and dynamism, highlighting the need for integrating multi-source data, handling diverse transportation modes, and modeling evolving traffic patterns. GNN models, while effective, face challenges in interpretability, requiring frameworks that combine traditional traffic theories with data-driven insights. Further, small sample learning and uncertainty quantification remain critical issues. The book offers future research recommendations, such as optimizing model efficiency, incorporating emerging technologies like federated learning, and improving interpretability, ultimately aiming to enhance traffic prediction for intelligent transportation and connected vehicle systems.