The field of traffic management and prediction is rapidly evolving, with a focus on developing innovative solutions to address the complexities of urban mobility. Recent research has emphasized the importance of fine-grained traffic inference, sparse bicycle volume estimation, and origin-destination extraction for tourism trend analysis. Graph neural networks have emerged as a promising approach for modeling intricate spatial relationships and predicting co-visitation patterns. Additionally, there is a growing interest in developing robust and scalable models for taxi fare prediction, accident severity prediction, and driver fatigue detection. Noteworthy papers in this area include: NAICS-Aware Graph Neural Networks for Large-Scale POI Co-visitation Prediction, which introduces a novel graph neural network that integrates business taxonomy knowledge to predict population-scale co-visitation patterns. STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction, which leverages adaptive graph construction and modality-aware attention mechanisms to capture complex relationships between spatial, temporal, and contextual variables.