Parkur: Digital Twin Parking System with Nearest-Slot Allocation and Edge AI-Based Occupancy Monitoring
DOI:
https://doi.org/10.5281/zenodo.24011818Keywords:
Digital Twin, Smart Parking, Automatic Number Plate Recognition, YOLO, Edge AI, Slot Allocation, Internet of ThingsAbstract
Conventional parking management still relies on manual observation or fragmented dashboards, so field conditions are difficult to monitor quickly and comprehensively. Existing digital parking systems typically display numbers, sensor statuses, or CCTV feeds separately, forcing operators to fuse information mentally; the result is low visibility of slot occupancy, limited monitoring parameters, and no interactive centralized visualization. This paper presents Parkur, a digital twin parking system that unifies the physical parking area, IoT data streams, and an interactive virtual environment in a plug-and-play architecture. The system is organized into eight layers: physical devices, device connectivity and integration, edge intelligence, data platform, digital twin core, analytics and decision, experience dashboard, and a closed feedback loop. Vehicle detection uses the YOLO family of detectors, multi-object tracking uses ByteTrack, and automatic number plate recognition combines YOLO-based plate detection with PaddleOCR, all executed on edge devices such as the NVIDIA Jetson Orin Nano. Slot occupancy is derived from a rule engine with geofencing, and a rule-based alert engine issues real-time notifications. A key innovation is the nearest-slot allocation algorithm, which assigns each entering vehicle the closest available slot to reduce cruising and disorder. The dashboard integrates a 3D visualization area, live CCTV preview, occupancy statistics, and a tap-to-inspect slot inspector. Compared with previous fragmented monitoring approaches, Parkur provides centralized interactive visibility purpose-built for parking operators. The paper details the layered architecture, the edge AI perception pipeline, and the allocation algorithm, and presents the implemented 3D dashboard of the prototype. The main contribution is a complete plug-and-play digital twin architecture for parking management that couples edge AI perception with nearest-slot allocation in one interactive environment.
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