SMART PARKING OCCUPANCY SYSTEM

Rapid urbanization, increasing vehicle ownership, limited parking infrastructure, and inefficient utilization of available parking spaces have created significant mobility challenges in cities, universities, shopping centers, hospitals, airports, residential complexes, and commercial facilities. Conventional parking management frequently depends on manual inspection, static signboards, entry tickets, security personnel, or driver-based visual searching, resulting in unnecessary circulation, traffic congestion, fuel consumption, time loss, carbon emissions, and poor user experience. This research proposes an intelligent Smart Parking Occupancy System that integrates parking-space sensors, cameraassisted monitoring, embedded processing, wireless communication, Internet of Things connectivity, occupancy analytics, real-time availability management, and user notification within a unified architecture. The proposed system continuously acquires parking information through ultrasonic sensors, infrared sensors, magnetic sensors, or camera-based detection mechanisms and determines whether individual parking spaces are Vacant, Occupied, Reserved, or Unavailable. Sensor readings are validated and processed through an edge controller to reduce noise, resolve temporary fluctuations, and generate reliable occupancy states. The parking information is transmitted through Wi-Fi, LoRaWAN, Zigbee, cellular communication, or other suitable network technologies to a centralized parking management platform. A real-time occupancy engine maintains the status of individual parking slots, calculates zone-level availability, detects abnormal occupancy behavior, and updates mobile applications, web dashboards, entrance displays, and navigation interfaces. The proposed architecture consists of five interconnected layers: Parking Environment and Data Acquisition, Edge Processing and Occupancy Intelligence, Communication and IoT Integration, Smart Parking Management and Decision, and User Application and Monitoring layers. The framework supports real-time space discovery, parking guidance, reservation management, occupancy visualization, historical analytics, administrative monitoring, and optional automated alerts. Illustrative conceptual evaluation indicates improved occupancy detection accuracy, precision, recall, F1-score, system reliability, and lower occupancy-update latency compared with manual parking management, conventional single-sensor systems, and basic IoT parking mechanisms. The proposed system provides a scalable foundation for intelligent parking management across smart cities, universities, shopping malls, hospitals, airports, offices, residential communities, and public transportation facilities.