The urban landscape is in flux. Traffic congestion, environmental concerns, and a growing desire for flexible transportation options are pushing cities to rethink mobility. Mobility-on-Demand (MoD) systems, such as Uber and Lyft, offer a promising opportunity by linking passenger requests with available vehicles through smartphone applications. However, the growth of these services raises concerns regarding congestion and emissions, necessitating advanced operational strategies. This dissertation investigates the integration of automated vehicles (AVs) within MoD systems to enhance fleet coordination and overall service efficiency. It addresses two key challenges: empty vehicle repositioning and ride-pooling route planning. The research focuses on innovative methods to capture the complex relationship between passenger demand and vehicle supply in dynamic mobility systems, proposing advanced control algorithms to optimize fleet operations.

This dissertation includes two main parts. In Part I, a novel distributed coverage control scheme is introduced to guide idle vehicles to high-demand areas. Initially formulated in continuous space and further extended to a graph-based representation which better captures the nature of urban road networks, the method treats the repositioning challenge as an area coverage problem, aligning vehicle distribution with passenger demand. Further, a hierarchical framework is developed to coordinate vehicle repositioning at both macroscopic and microscopic scales. At the higher level, a data-driven predictive control algorithm is mainly described to manage inter-regional vehicle transfers, while the lower level leverages node-level position guidance to control individual vehicle movements. This framework effectively coordinates between the actions of the high-level controllers, which manage aggregated traffic components, and the self-governance of individual vehicles at the lower level. Part II extends the study to ride-pooling systems, where vehicles serve multiple passengers via one pooled trip. A theoretical and numerical study validates the potential benefits of detouring partially-occupied vehicles, which enhances their likelihood of matching with additional passengers. The pool-match probability between passengers and one partially occupied vehicle is modeled. Building on this foundation, a Mixed Integer Linear Programming (MILP) algorithm is developed to construct optimal detour paths by evaluating candidate road segments. It can adapt to fluctuating demand and traffic conditions. To evaluate the proposed methods, an operational, agent-based Mobility-on-Demand simulator is implemented, enabling analysis under diverse experimental scenarios. Simulation results demonstrate significant improvements, including a higher request answer rate, reduced waiting times, minimized empty travel distances, and enhanced profitabilityâ achieving a win-win-win outcome for customers, service providers, and the environment.