We propose a new “smart parking” system for an urban environment. The system assigns and reserves an optimal parking
space for a driver based on the user’s requirements that combine 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. Implementation issues including parking detection,
reservation guarantee and Vehicle-to-Infrastructure (V2I) or Infrastructure-to-Vehicle (I2V) communication are resolved in
the paper. Our system can save driver time, fuel and expense, while reducing the traffic congestion and environment
pollution. We also describe a deployment and testing pilot study of the system in a garage at Boston University