This project aims to develop a demand-driven approach for shared mobility operations with machine learning and math programming methods. The objective of this approach is to incorporate economic, environment and equity impacts over an entire operational cycle. Both ride-hailing systems (e.g. Lyft) and ride-pooling systems (e.g. UberPool) are investigated. The developed models are always tested with real-world taxi data including detailed trajectories of vehicles and their loading states.