The field of urban mobility and transportation systems is experiencing significant innovations, driven by the need for more efficient, sustainable, and user-centric solutions. Recent research has focused on developing predictive path planning algorithms for dynamic rebalancing in ride-hailing systems, which can anticipate and adapt to changing demand patterns. Another area of exploration is the incorporation of passenger comfort and behavioral alignment into ride-hailing systems, leading to more personalized and socially acceptable matchings. Data-driven approaches are also being applied to compare and analyze urban forms across different cities, revealing functionally convergent urban forms and highlighting the importance of spatial scale in cross-city comparisons. Additionally, geospatial and temporal trend analysis is being used to optimize urban transportation systems, including fleet management and resource allocation. Noteworthy papers in this area include: Wise Goose Chase, which proposes a predictive path planning framework for dynamic rebalancing in ride-hailing systems. Maximal Compatibility Matching, which presents a novel assignment strategy that incorporates passenger comfort into the matching process. Urban Forms Across Continents, which develops a data-driven framework for comparing urban typologies across geographically and culturally distinct cities.