Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles (AVs), however, the characteristics of mobility services change, and new opportunities to overcome the prevailing limitations arise. This thesis proposes a series of learning- and optimization-based strategies to help autonomous transportation providers meet the service quality expectations of diversified user bases. We show how autonomous mobility-on-demand (AMoD) systems can develop to revolutionize urban transportation, improving reliability, efficiency, and accessibility.