Abstract:

Urban parking systems have long been an integral part of city infrastructure, evolving over time to accommodate growing urban populations and increasing vehicle ownership. Traditional parking management systems relied on static signage, manual enforcement, and limited data collection, making it difficult to efficiently allocate parking resources. With the rise of smart technologies, the parking industry has gradually shifted towards automated systems and digital solutions. Traditional urban parking systems typically involve the allocation of parking spaces based on fixed zones or manual entry. These systems often require drivers to search for available parking, leading to traffic congestion, wasted fuel, and unnecessary emissions. Furthermore, manual enforcement of parking rules leads to inefficiencies in managing parking space usage and improper parking practices. There is little real-time data on parking availability, which further exacerbates the problem. challenge is to optimize the use of limited urban parking spaces while minimizing congestion and environmental impact. Current systems fail to provide real-time parking information, often leading to inefficient utilization of available spaces and longer search times for drivers. This results in increased fuel consumption, environmental pollution, and a frustrating experience for city residents. With urban areas growing rapidly, it is essential to adopt more intelligent and efficient systems to improve parking management. The integration of Machine Learning (ML) in parking systems offers an opportunity to enhance the user experience, optimize parking space allocation, reduce traffic congestion, and minimize environmental impacts. Leveraging data-driven models will enable cities to predict parking demand, manage occupancy dynamically, and optimize space usage in real-time. The proposed system integrates machine learning algorithms with real-time data from sensors, cameras, and mobile applications to predict parking demand and availability. By utilizing historical data and traffic patterns, the system will allocate parking spaces efficiently, reduce search times, and dynamically adjust parking fees based on demand, leading to improved urban parking efficiency.

Description:

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