URBAN PARKING OPTIMIZATION THROUGH INTEGRATED MACHINE LEARNING MODELS

In order to handle expanding urban populations and rising car ownership, urban parking facilities have long been a crucial component of metropolitan infrastructure. It was challenging to distribute parking resources effectively with traditional parking management systems since they depended on static signs, human enforcement, and limited data collecting. The parking business has steadily moved towards automated systems and digital solutions as a result of the development of smart technology. Parking spots are usually assigned via manual input or defined zones in traditional urban parking systems. These systems often force cars to look for parking spaces, which causes traffic jams, fuel waste, and needless emissions. Additionally, ineffective parking space management and inappropriate parking behaviours result from the manual enforcement of parking regulations. The issue is made worse by the paucity of real-time parking availability data. Optimising the utilisation of scarce urban parking spots while reducing traffic and environmental effect is a problem. The lack of real-time parking information provided by current systems often results in wasteful use of available spots and lengthier search durations for vehicles. For city dwellers, this leads to a frustrating experience, higher gasoline use, and environmental pollution. Adopting more sophisticated and effective solutions is crucial to improving parking management in metropolitan areas that are expanding quickly. Parking systems that use machine learning (ML) have the potential to improve user experience, allocate parking spaces optimally, lessen traffic, and have a less environmental effect. Cities will be able to forecast parking demand, dynamically control occupancy, and optimise space utilisation in real-time by using data-driven algorithms. In order to forecast parking demand and availability, the proposed system combines machine learning algorithms with real-time data from sensors, cameras, and mobile applications. By employing traffic patterns and historical data, the system will effectively distribute parking spaces, shorten search times, and dynamically modify parking fees in response to demand, improving urban parking efficiency.