A User-Oriented Adaptive-Optimal Car Parking Management System Towards Smart Livings

Existing parking management approaches do not consider specific requirements, priorities, user comfort, or modes of use when allocating a parking spot in a large park. As a result, vehicles carrying multiple passengers but staying for a limited period often have to drive further, searching for a parking spot, which increases fuel consumption, emissions, waste of time, and discomfort of users due to extra walking distance. In this paper, we consider the need for both sustainability and comfortable livings in a future smart city and propose an adaptive-optimal scheme that takes advantage of parking efficiency based on the passenger information and flexibly provides the optimal parking spot to the individual. We presume that the management system has information about the number of users, user priority, and expected stay time when a car arrives or a parking request is made. The best parking slot is assigned based on the available parking slots and the given objectives, such as the shortest travel distance inside the parking zone for a low pollution, the shortest walking distance per user, or a combination of both with some trade-off. The decision process is fine-tuned using parking data obtained from a model of a large car park of a shopping complex, and the results of the proposed scheme are compared with other schemes. The findings indicate that overall time spent in the parking lot, as well as individual walking and travel distances, have significantly improved.