A Multi-Criteria On-Street Parking Recommender System based on PSO algorithm

In recent years, the urbanization has clearly emphasized the need to pay attention to design and deployment of smart cities to reduce carbon dioxide emissions and increase energy storage. The great development of the number of vehicles on the road, along with the mismanagement of accessible parking space, has created problems about parking and increased traffic congestion in urban areas. Therefore, it is necessary to create an automatic smart parking management system that not only helps the drivers to find the appropriate parking space for their vehicles, but also reduces fuel consumption. Today, the biggest challenge is about the group of parking space on-street, which consists of two parts: identifying empty parking space in the city and proposing suitable parking space for drivers according to their criteria. The purpose of this article is to find a suitable parking space for drivers according to their specified criteria and recommend the most similar ones. When drivers plan to park on the side of the road, many criteria are considered, such as distance from the intended destination, payment, finding a parking space faster, etc. In a few articles, the issue of finding a parking space has been considered based on various criteria of drivers. In the proposed method, it is assumed that the parking spaces in the city are stored in a database and being updated constantly, Therefore, using the particle swarm optimization algorithm (PSO), drivers are offered the most suitable parking spaces according to their criteria. The implementation of this method also proved that when using PSO algorithm, although more time is spent, more requests are answered and less distance is traveled.