The recent advancements in trajectory prediction and anomaly detection have significantly enhanced the modeling of urban and human mobility. Researchers are increasingly focusing on integrating contextual information, such as agent identities and geographic points of interest, to improve the accuracy and robustness of anomaly detection systems. This context-aware approach is proving to be more effective in capturing anomalous behaviors by leveraging additional data sources. Additionally, multi-agent trajectory prediction is being advanced through innovative methods that incorporate patching-based temporal feature extraction and explicit modality modulation, which address the complexities of human behavior and social interactions. These methods are demonstrating superior performance in public benchmarks, indicating a promising direction for future research. Furthermore, the introduction of temporal ensembling with learning-based aggregation is mitigating the issue of missing behaviors in trajectory prediction, leading to more consistent and diverse predictions. This approach, validated on datasets like Argoverse 2, shows significant improvements in key metrics such as minADE and minFDE. Unified trajectory modeling frameworks, such as TrajAgent, are also emerging, offering a comprehensive solution for various trajectory tasks by leveraging large language models and systematic optimization modules. These frameworks are proving effective across diverse datasets, achieving notable performance improvements over baseline methods. Lastly, heterogeneous interaction modeling is being refined to reduce accumulated error in multi-agent systems, addressing the complexities of dynamic interaction graphs and proposing novel strategies like graph entropy and mixup training to enhance prediction accuracy. Overall, these developments are pushing the boundaries of trajectory prediction and anomaly detection, making significant strides towards more intelligent and context-aware systems.