Ai-Based Smart Parking And Traffic Prediction Systems: A Review Of Occupancy Detection, Availability Forecasting And Spatio-Temporal Traffic Models

Urban growth and rising vehicle numbers have created severe traffic congestion and shortages of parking space, leading to longer travel times, higher fuel consumption, pollution and driver frustration; a significant share of urban traffic consists of drivers searching for parking. This paper reviews AI-based smart parking and traffic prediction systems. It examines parking occupancy detection with in-ground sensors and with camera images classified by deep convolutional networks on public parking datasets, prediction of future parking availability with spatio-temporal models, and traffic flow forecasting from classical regression and decision trees to deep learning and graph neural networks that model road networks. The roles of cloud computing and mobile applications in guiding drivers are discussed. A five-layer architecture combining parking and traffic sensing, data integration, an AI prediction engine, a recommendation and guidance layer and user and authority applications is proposed, with research gaps, objectives, a comparison of techniques and challenges such as sensor cost, data quality, privacy and integration with existing infrastructure. The review concludes that integrating parking availability with traffic prediction can reduce cruising, waiting time and emissions while improving the use of parking facilities.