Smart Parking and Automation Based System as Dynamic Approach

Efficient management of growing urban parking demand would require smart parking systems. A dynamic approach will be presented: using IoT-enabled algorithms, predictive analytics, and processing real-time data to optimize space utilization in the parking lot and provide convenience for users while allowing for the maximum possible operational revenue. The system achieves this by utilizing IoT sensors such as ultrasonic, infrared, and magnetic field sensors to accurately detect the occupancy of parking spaces. Algorithms for sensor data aggregation preprocess and fuse the information so that it can be relied upon and, as a result, allows for real-time monitoring. Dynamic reservation and pricing algorithms are built into the system that balances parking fees according to real-time demand, applying techniques like surge pricing, time-based adjustments, and predictive forecasting to balance occupancy and maximize revenue. Predictive analytics further supports demand forecasting by using historical data, user behavior, and environmental factors, allowing for advanced resource allocation and proactive price adjustment during peak hours. Further enhancements are added to the system in the form of edge and corner detection techniques for better vehicle identification and reservation management. The dynamic capabilities of the proposed system enhance the efficiency of operation, improve the user experience by allowing real-time spot allocation and reservations, and reduce parking congestion.