摘要: Modern and intelligent urban designs pay much attention to parking spot positioning. The issue of allocating spaces for parking has been the subject of a great deal of research. However, most of these efforts rely on models constructed from historical data stored in centralized databases. This study proposed an automated parking assignment system using a combination of ensemble learning mechanisms and Boosting methods. The suggested automated parking allotment technique, with its focus on digital society, has the potential to become a crucial tool for managing parking in modern urban environments. The suggested system's hybrid ensemble-boosting mechanisms distribute parking spaces to customers based on their current locations and the availability of slots in a centralized database. The performance evaluation looks at how well the boosting technique works and how well the ensemble learning prediction works. It finds that extreme gradient boosting combined with random forest gives a 98.8% success rate. To ensure that customers and administrators have access to reliable and efficient parking options, the suggested strategy optimizes charging and allotment.