This thesis addresses challenges in modeling and control of multi-agent systems for future urban mobility, where strategic competition, decentralized coordination, and uncertainty are inherent. Motivated by applications in electric ride-hailing markets and large-scale traffic monitoring, the work develops principled frameworks that combine tools from game theory, optimization, and learning-based control.

The first part investigates the management of electric ride-hailing fleets in competitive markets. We propose bi-level formulations for optimal electricity pricing, modeled as Stackelberg and Reverse Stackelberg games, and establish conditions for equilibrium existence and uniqueness. Building on these results, we design distributed algorithms with provable convergence to local Stackelberg equilibria, along with learning-based extensions using bandit methods and no-regret learning, which eliminate the need for explicit information sharing between operators and regulators. Beyond single-shot settings, we introduce a new class of multi-stage resource allocation games inspired by Tullock contests, modeling charging scheduling and fleet rebalancing decisions in systems where profitability depends on supply-demand balance. For this class, we prove the uniqueness of Nash equilibria, show that it generalizes receding-horizon and Blotto-type games, and derive analytical solutions in the latter case.

The second part focuses on cooperative traffic monitoring with fleets of aerial drones. We develop a Gaussian process-based framework that fuses historical and real-time data to assign adaptive monitoring priorities across urban regions. This framework is integrated with centralized path-planning via distribution matching and decentralized strategies based on scalable multi-agent reinforcement learning. To account for temporal dependencies, we investigate recurrent neural architectures for implementing shared drone policies in adaptive patrolling.

Taken together, the contributions of this thesis advance the theoretical and algorithmic foundations of multi-agent control under competition, cooperation, and uncertainty. While grounded in applications to smart mobility, the developed models and methods extend to broader domains where strategic interaction and data-driven adaptation are key to effective system management.