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We propose a new “smart parking” system for an urban environment. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the user's objective function that combines proximity to destination and parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. The allocation is updated at the next decision point ensuring that there is no resource reservation conflict and that no user is ever assigned a resource with higher than the current cost function value. Simulated case studies are included based on parking at part of the Boston University campus showing that we can achieve significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems. We also describe a laboratory setting where this system has been tested in real time.