Abstract This study paved the way for developing digital twins of smart and emerging urban mobility systems, using shared mobility services such as ridesharing as a key case study. As cities contend with challenges, such as traffic congestion, environmental sustainability, and transportation equity, shared mobility platforms (e.g., UberPOOL and Lyft Shared) have emerged as promising solutions. Leveraging Chicago’s Transportation Network Companies (TNCs) shared mobility data set, this research uncovers latent patterns in user behavior and trip-sharing dynamics through data mining and exploratory analysis. It distinguishes between trips, where users authorized ride-sharing and those that were actually pooled, revealing key spatial, temporal and behavioral difference. Economic factors also played an important role. For instance, the hourly gap between authorized and successfully pooled trips was narrower on weekends, suggesting more stable matching opportunities, while users who authorized but were not pooled tended to pay less per mile than the general trip population. Building on these insights, this study integrates both supervised and unsupervised machine learnin
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