This talk presents the design of ridesharing hub networks to promote shared transportation. Given a set of passenger trips in an urban area, the problem is to determine the origins and destinations of a fixed number of ridesharing connections to maximize the potential users of the system. The problem is formulated as a maximal covering hub arc location model and solved to optimality using Benders decomposition. Several algorithmic enhancements, including using reduction tests to eliminate variables and adding multiple Pareto-optimal cuts, are proposed to improve the convergence of the Benders decomposition algorithm. Additionally, two data-driven clustering-based methodologies are adapted and implemented to compare with the solutions of the optimization model. All methodologies are tested using the New York City taxi trip data. Several computational experiments are conducted to compare optimization and data-driven approaches under key performance metrics that include the total number of commuters that use the ridesharing system, the percentage of satisfied trips by ridesharing, the utilization of ridesharing trips, and the driving and walking distances. The results from the optimization model yield more satisfied trips through the ridesharing system and also a more balanced utilization of the ridesharing connections compared with the results obtained from either of the clustering-based methodologies. Our results show that operating even a few ridesharing connections in the city of New York can result in reducing a significant number of individual rides and the total driving distance of the commuters without incurring a walking distance above 1 km per passenger. Municipalities or ridesharing companies can utilize our models to decide on the most promising ridesharing connections to set up shuttles for ridesharing