Finding a best parking place using exponential smoothing and cloud system in a metropolitan area

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.