Integrating shuttles into an urban transit system can play a pivotal role in creating safer, sustainable, accessible, affordable, and less congested urban environments. This paper studies the design of hub networks for strategic deployment of autonomous shuttles. Given a set of passenger trips in an urban area, the problem is to determine the origins and destinations of a fixed number of hub arcs that represent shuttle 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 and validate 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 shuttle system, the percentage of satisfied trips by these shuttles, the utilization of the shuttles, and the driving and walking distances. The results from the optimization model yield more satisfied trips through the system and also a more balanced utilization of the shuttles compared with the results obtained from either of the clustering-based methodologies.