This master's project studies the role of ride-splitting incentives in the service level, total revenue, and trac impact of a ride-sourcing platform, which is built as a discrete event simulator using simulated taxi data within a congestible road network of a megacity Shenzhen, China. After calibrating passenger choice models to re ect riders' preferences for a trade-o between cost and travel time, two request-level pricing strategies are developed to maximize the expected revenue from a trip shared between two riders. Then, simulation-level pricing strategies are designed to accommodate temporal and spatial demand patterns. Experimental results show that ride-splitting incentives based on customer preferences can improve service level and trac condition over high-demand periods, and may perform as a sustainable regulatory measure to rebalance eet in high-abandonment regions. These incentives are also more robust to variation in passenger preferences compared to xed-rate discounts. However, in order to align with the platform's prot-driven motives, pricing strategies should be accompanied by monitoring of trac dynamics as well as restriction on eet size.