The recent advancements in urban mobility and traffic analysis have seen a shift towards more integrated and context-aware models. Researchers are increasingly focusing on capturing the nuanced relationships between various urban factors such as geographic information, built environment, and temporal dynamics to enhance prediction accuracy. Notably, there is a growing emphasis on leveraging machine learning and graph neural networks to model complex, non-linear interactions that traditional methods often overlook. This trend is particularly evident in the development of models that can handle degraded weather conditions and asynchronous inflow tracking, which are critical for real-world applications. Additionally, the integration of contrastive learning and advanced station semantics representation is proving to be a powerful tool for enriching the contextual understanding of passenger mobility patterns. These innovations not only improve the precision of origin-destination flow predictions but also offer better explainability of regions' functions using nominal attributes. The field is moving towards more sustainable and efficient urban planning strategies, with a strong focus on reducing car dependency and optimizing public transportation systems. Notably, the incorporation of geographic information alignment and the exploration of non-linear effects of the built environment on travel are emerging as key areas of advancement.