AI-Powered Parking Infrastructure And Vehicle Identifications System For Modern Smart Cities

Parking plays a pivotal role in urban transportation by ensuring organized vehicle management in densely populated areas. However, the rapid surge in vehicle ownership has rendered traditional parking systems increasingly inefficient. Common issues include poor utilization of parking spaces, long wait times to find slots, added traffic congestion, and unnecessary fuel consumption. Additionally, many existing parking prediction systems fall behind due to their dependence on outdated methods, lack of live data integration, and limited ability to scale. Security is another critical concern, particularly in high-risk zones such as residential communities, government facilities, and military premises, where unauthorized access and vehicle theft remain major threats. To overcome these limitations, this project introduces an intelligent Smart Parking System powered by Edge Computing and Deep Learning. This system connects multiple parking areas through a cohesive network, enabling dynamic slot assignment and real-time updates on space availability. For enhanced security, Convolutional Neural Networks (CNNs) are utilized for facial recognition, accurately verifying vehicle owners during entry and exit to prevent unauthorized access and enhance theft protection. The proposed solution features an automated and centralized vehicle identification process that delivers both secure and convenient parking operations. Through real-time processing and an intuitive user interface, drivers are promptly informed about their access permissions and guided to available parking spaces. This innovative system not only boosts parking efficiency in urban settings but also strengthens security infrastructure, offering a forward-thinking response to contemporary parking challenges.