New “Smart Parking” System Based on Resource Allocation and Reservations

TLDR

The authors propose a novel smart parking system for urban environments that assigns optimal parking spaces based on driver cost functions. The system solves a mixed‑integer linear programming problem at each time‑driven decision point, allocating optimal parking spaces while ensuring no reservation conflicts and that drivers are never assigned a higher‑cost space than their own cost function. Simulation and garage implementation show that the system reduces average parking search time and cost while improving overall capacity utilization compared to uncontrolled or guidance‑based approaches.

Abstract

We propose a novel "smart parking" system for an urban environment. The system assigns and reserves an optimal parking space based on the driver's cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this driver's current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.