Advanced IoT-integrated parking systems with automated license plate recognition and payment management

Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occu...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 2388 - 17
Main Authors: Pradhan, Gulmini, Prusty, Manas Ranjan, Negi, Vipul Singh, Chinara, Suchismita
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 18.01.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/987
639/705/117
Accuracy
Algorithms
Automation
Cameras
Fare calculation and management
Humanities and Social Sciences
Image processing
Image processing algorithms
Intelligent transportation systems
Internet of things (IoT)
multidisciplinary
Science
Science (multidisciplinary)
Sensors
Smart parking management
Vehicle detection and tracking
Fare calculation and management
Vehicle detection and tracking
Smart parking management
Image processing algorithms
Intelligent transportation systems
Internet of things (IoT)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occupancy during peak times, creating congestion and billing inaccuracies. This research proposes an automated system integrating sensors, image processing, and database management to address these issues. A single camera monitors multiple parking slots, with predefined coordinates linked to IR sensors for dual verification. Image processing algorithms, including Optical Character Recognition (OCR), enable accurate license plate recognition. Testing under real-world conditions showed 95% accuracy in daylight, 90% in low light, and 93% for plates at 45-degree angles. Detection accuracy reached 88% at distances of 1.5–3 m, ensuring reliable operation even at the camera’s range limits. Occupancy tracking achieved less than a 5% error margin compared to manual methods, while the fare calculation module reduced billing errors by 90%, enhancing efficiency and revenue. The system’s scalable design supports applications in parking management, toll collection, and traffic monitoring. By improving vehicle detection, occupancy tracking, and billing accuracy, this solution addresses critical challenges in urban parking and contributes to smarter city infrastructure.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-86441-w