This thesis proposes an integer programming model for Dynamic Taxi-Sharing (DTS), which allows two groups of taxi users to ride on the same taxi together. The model matches taxi drivers and user pairs in certain sequences with the goal of maximizing taxi providers’ profit. We also develop a DTS fare calculation scheme which can automatically calculate the fare for each DTS user and self-adjust to balance the taxi occupancy rate in real time. A customized spectral clustering approach for preselection on DTS trips is also designed to narrow down the search space for the model. Real-world taxi trip data is used to demonstrate the DTS system is beneficial to providers, taxi users, and taxi drivers.