The field of urban mobility and transportation systems is moving towards optimizing usage and experience through innovative solutions. Researchers are exploring ways to balance efficiency and equity in ride-sourcing services, ensuring that vehicles are rebalanced to meet demand while maintaining accessibility for all users. Another direction is the integration of autonomous mobility-on-demand and micromobility systems, which aims to optimize passenger travel time and increase the efficiency of transportation networks. Noteworthy papers include Optimizing Library Usage and Browser Experience, which presents a simulation framework to optimize book reservations and improve browser experience, and Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantees via Constrained Mean-Field Reinforcement Learning, which introduces a scalable continuous-state mean-field control model to ensure equitable service distribution. The paper Repositioning, Ride-matching, and Abandonment in On-demand Ride-hailing Platforms provides a comprehensive mean field game model to analyze the dynamics of ride-hailing platforms and proposes a novel two-matching-radius nearest-neighbor dispatch algorithm to mitigate inefficiencies.