Digital Twinning Mechanism and Building Information Modeling for a Smart Parking Management System

Abstract

Highlights: What are the main findings? This study demonstrates a developmental framework for a smart parking management system (SPMS) digital twinning capability through a cross-platform-based digital twinning mechanism using machine vision and building information modeling for the spatial visualization of parking occupancy data within the built environment. The digital twin (DT) system streamlines You Only Look Once version 7 (YOLOv7), Object Detection (OD), and Deep Text Scene Text Recognition Inferences (DTR-STR) into a database pipeline, supporting automated vehicle profiling (VP) and data analytics on vehicle activity within the built environment. What is the implication of the main finding? The DT framework explored in this study aligns with existing and emerging smart city trends such as artificially intelligent buildings in integrating building information models (BIMs) with building data, potentially facilitating facility management and data-driven decision-making. This study serves as a demonstration of how existing parking infrastructures can receive automation interventions and have their capabilities scaled up for applying BIM-based DT models to broader urban contexts. Parking space shortages are attributed to an increased density of vehicle presence in the urban context, necessitating the implementation of effective parking management strategies, especially in areas where facility expansion is constrained by limited land availability. Many parking facilities remain operationally inefficient and underutilized due to manual VP methods and having little access to parking resource utilization data. This study develops a DT-based SPMS integrating machine vision, data modeling, and DT technology to automate facility management operations. The system uses YOLOv7 for vehicle and License Plate Detection (LPD), and Deep Text Recognition–Scene Text Recognition (DTR-STR) for license plate recognition (LPR). The findings indicate an 89.89% accuracy for VP- and LPR-based occupancy tracking tasks, and 94.86% for vehicle detection or VD-based occupancy tracking. The system in the built environment comprises three features: (1) automated VP at parking entry and exit points, (2) occupancy monitoring through LPR, (3) Object Detection (OD) for occupancy tracking. The 3D BIM DT model in Autodesk Revit processes inference data from machine vision models to visualize parking activity.