Abstract Smart Cities are examples of Cyber-Physical Systems whose goals include improvements in transportation, energy distribution, emergency response, and infrastructure maintenance, to name a few. When it comes to mobility, the availability of large amounts of data, ubiquitous wireless connectivity, and the critical need for scalability open the door for new control and optimization methods with the aim of automating all aspects of mobility, from interconnected self-driving vehicles to sharing transportation resources. We address two key questions: can control and optimization methods enable this automation and, if so, how can we quantify its benefits to justify the challenging technological, economic, and social transitions involved? An optimal control framework is presented to show how Connected Automated Vehicles (CAVs) can operate in a dynamic resource contention environment, primarily urban intersections without any traffic lights. We also describe how large amounts of actual traffic data can be harnessed and drive inverse optimization methods to quantify the value of CAVs in terms of eliminating the prevailing Price of Anarchy: the gap between current “selfish” user-centric and optimal “social” system-centric traffic equilibria which are achievable with automated mobility.