Dynamic resource allocation in urban settings: A “smart parking” approach

Abstract

We propose a “smart parking” system for an urban environment based on a dynamic resource allocation approach. 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 with 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, that no user is ever assigned a resource with higher than the current cost function value, and that a set of fairness constraints is satisfied. We add an event-driven mechanism to compensate for users with no assignment that are close to their destinations. Simulation results show that using this “smart parking” approach can achieve near-optimal resource utilization and significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems.