Trajectory Prediction (TP) plays a pivotal role across various domains, transforming navigationand interaction within complex environments. This study introduces the Semantic Aware Environ-mental Spatial-Temporal Graph Transformer (SAE-STAR) model, designed for multi-class TP in dy-namic urban settings. By leveraging deep learning and environmental infrastructure data, the modelforecasts the movements of pedestrians, bicyclists, and diverse vehicle types. Research questions ex-plore enhancing prediction accuracy through infrastructure data, optimization of multi-class models,and forecasting agent-infrastructure interactions. Integrating graph-based and deep learning techniquesaims to overcome existing TP model limitations, contributing to more accurate and reliable predictionsystems. Empirical studies and real-world experiments provide insights into TP capabilities, limita-tions, and potential impacts on intelligent systems and decision-making processes. The study identifiesimprovements in prediction accuracy with environmental data integration, notably demonstrating thesuperior performance of the SAE-STAR model on the Valhallavägen dataset compared to SemanticAware Spatial Temporal Graph Transformer (SA-STAR). Challenges include class imbalance effects,complexities in static feature incorporation, and hyperparameter tuning difficulties. Quantitative analy-sis shows an effective prediction of linear trajectories but challenges in complex scenarios corroboratedqualitatively. Future work entails refining model architectures, extensive hyperparameter optimization,and enhancing data collection methodologies to improve TP model robustness in urban environments.