Advanced IoT-integrated parking systems with automated license plate recognition and payment management
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86441-w |
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| author | Gulmini Pradhan Manas Ranjan Prusty Vipul Singh Negi Suchismita Chinara |
| author_facet | Gulmini Pradhan Manas Ranjan Prusty Vipul Singh Negi Suchismita Chinara |
| author_sort | Gulmini Pradhan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-9edb6202c3e842c4bc1b7b4994ee74f9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9edb6202c3e842c4bc1b7b4994ee74f92025-01-19T12:18:31ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-86441-wAdvanced IoT-integrated parking systems with automated license plate recognition and payment managementGulmini Pradhan0Manas Ranjan Prusty1Vipul Singh Negi2Suchismita Chinara3School of Computer Science and Engineering, Vellore Institute of TechnologyCentre for Cyber Physical Systems, Vellore Institute of TechnologyDepartment of Computer Science and Engineering, National Institute of TechnologyDepartment of Computer Science and Engineering, National Institute of TechnologyAbstract 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.https://doi.org/10.1038/s41598-025-86441-wIntelligent transportation systemsVehicle detection and trackingImage processing algorithmsInternet of things (IoT)Fare calculation and managementSmart parking management |
| spellingShingle | Gulmini Pradhan Manas Ranjan Prusty Vipul Singh Negi Suchismita Chinara Advanced IoT-integrated parking systems with automated license plate recognition and payment management Scientific Reports Intelligent transportation systems Vehicle detection and tracking Image processing algorithms Internet of things (IoT) Fare calculation and management Smart parking management |
| title | Advanced IoT-integrated parking systems with automated license plate recognition and payment management |
| title_full | Advanced IoT-integrated parking systems with automated license plate recognition and payment management |
| title_fullStr | Advanced IoT-integrated parking systems with automated license plate recognition and payment management |
| title_full_unstemmed | Advanced IoT-integrated parking systems with automated license plate recognition and payment management |
| title_short | Advanced IoT-integrated parking systems with automated license plate recognition and payment management |
| title_sort | advanced iot integrated parking systems with automated license plate recognition and payment management |
| topic | Intelligent transportation systems Vehicle detection and tracking Image processing algorithms Internet of things (IoT) Fare calculation and management Smart parking management |
| url | https://doi.org/10.1038/s41598-025-86441-w |
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