Intelligent Parking Management Systems Using IoT and Machine Learning Techniques for Real-Time Space Availability Estimation

Traffic jams and drivers looking for parking spaces are caused by the increased urbanization and vehicle population. This paper proposes an intelligent parking guidance system that effectively manages spots for parking by using the Internet of Things (IoT) and Machine learning methods. The system uses IoT sensors and cameras positioned in parking lots to track the occupancy status of specific parking spots in real-time. The acquired data is sent to a cloud server for analysis and processing. The Convolutional Neural Network (CNN) algorithm, a deep learning approach, is used to evaluate the camera images and accurately determine if parking spots are occupied. The number of parking spots available, location directions, and expected arrival times may all be accessed by drivers using a user-friendly smartphone application. Advanced features such as requests, payment, and navigation integration may also be added to the system to improve the parking experience.