Digital Twin-Based Smart Parking Management Research Topics
Urban areas face increasing parking challenges due to rising vehicle density, limited land availability, and operational inefficiencies in existing parking facilities. Traditional manual vehicle parking (VP) methods often result in underutilization and poor management of parking resources. This research explores the integration of Digital Twin (DT) technology, Building Information Modeling (BIM), and machine vision to create an automated, intelligent parking management system. By leveraging real-time data and advanced detection algorithms, the study aims to enhance parking efficiency, monitoring, and visualization within urban built environments.
Vehicle Detection and License Plate Recognition Integration
A crucial aspect of the system is the use of YOLOv7 for vehicle detection (VD) and Deep Text Recognition–Scene Text Recognition (DTR-STR) for license plate recognition (LPR). These technologies allow for precise tracking of vehicles entering and exiting parking facilities. Integrating VP and LPR data ensures accurate occupancy monitoring, reduces human error, and improves operational efficiency in managing urban parking spaces.
Digital Twin Implementation in Parking Facilities
The 3D BIM-based DT model in Autodesk Revit processes inference data from machine vision systems to provide a visual and interactive representation of parking activities. This integration allows facility managers to monitor real-time occupancy, predict space availability, and optimize parking layouts dynamically. DT serves as a bridge between physical parking infrastructure and intelligent digital monitoring, creating a robust framework for smart urban mobility management.
Automated Parking Entry and Exit Management
Automating the VP process at parking entry and exit points significantly reduces operational bottlenecks and human intervention. By leveraging machine vision and DT integration, vehicles can be detected and tracked in real-time, enabling seamless automated entry/exit and reducing congestion. This approach ensures both accuracy and convenience for urban parking management while enhancing user experience.
Occupancy Monitoring and Optimization
Accurate occupancy monitoring is achieved through the combination of LPR-based tracking and object detection (OD) methods. The system demonstrates high accuracy for both vehicle detection (94.86%) and occupancy tracking via VP and LPR methods (89.89%). Continuous monitoring provides actionable insights for facility managers, allowing for optimization of parking allocation, demand forecasting, and real-time decision-making to maximize efficiency.
Implications for Smart Urban Mobility
The integration of machine vision, BIM-based digital twins, and automated parking management systems has significant implications for smart city development. Beyond optimizing parking operations, this approach enhances urban mobility, reduces traffic congestion, and supports data-driven urban planning. The research establishes a framework that can be extended to other urban infrastructure systems, promoting sustainability and intelligent resource management.
Architecture Engineers Awards
🔗 Nominate now! 👉 https://architectureengineers.com/award-nomination/?ecategory=Awards&rcategory=Awardee
🌐 Visit: architectureengineers.com
📩 Contact: contact@architectureengineers.com
Get Connected Here:
*****************
Instagram : https://www.instagram.com/architecture_engineers_awards/
Facebook : https://www.facebook.com/profile.php?id=61576995475934
Tumblr : https://www.tumblr.com/blog/architectureengineers
Pinterest : https://in.pinterest.com/researcherawards123/
Blogger : https://architectureengineers.blogspot.com/
Twitter : https://twitter.com/
#SmartParking
#DigitalTwin
#BIM
#MachineVision
#YOLOv7
#LicensePlateRecognition
#DeepTextRecognition
#SceneTextRecognition
#VehicleDetection
#UrbanMobility
#ParkingManagement
#AutomatedParking
#OccupancyMonitoring
#BuiltEnvironment
#SustainableUrbanPlanning
#SmartCity
#DataDrivenDesign
#UrbanInfrastructure
#IntelligentSystems
#RealTimeMonitoring
Comments
Post a Comment